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Recent developments in statistics and data science : SPE2021, Évora, Portugal, October 13-16 / / Regina Bispo [and three others], editors
| Recent developments in statistics and data science : SPE2021, Évora, Portugal, October 13-16 / / Regina Bispo [and three others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (364 pages) |
| Disciplina | 519.5 |
| Collana | Springer proceedings in mathematics & statistics |
| Soggetto topico |
Mathematical statistics
Estadística matemàtica Models matemàtics |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-031-12766-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Organization -- Welcome Message from the Editors -- Contents -- How to Increase the Visibility of Statisticians in the Modern World of Dataism? -- 1 Introduction -- 2 Statistical Leadership and Its Key Competences -- 2.1 Active Listening -- 2.2 Networking -- 2.3 Effective Communication -- 3 Increasing Visibility in Academia -- 4 Increasing Visibility in Society -- 5 Concluding Remarks -- References -- A Robust Hurdle Poisson Model in the Estimation of the Extremal Index -- 1 The Extremal Index -- 1.1 Motivation -- 1.2 Theoretical Introduction -- 1.3 EI Estimators -- 1.4 Scope of the Article -- 2 The Hurdle Model -- 2.1 Why the Hurdle Poisson Model? -- 3 Robust Estimation of the Hurdle Model -- 4 Simulation Study -- 4.1 Simulated Scenarios -- 4.2 Software Tools -- 5 Analysis of Results -- 6 Final Comments -- References -- Computational Study of the Adaptive Estimation of the Extreme Value Index with Probability Weighted Moments -- 1 Introduction and Scope of the Article -- 1.1 EVI-Estimators Under Consideration -- 1.2 Scope of the Article -- 2 Adaptive EVI-Estimation and the Bootstrap Methodology -- 2.1 The Bootstrap Methodology in Action -- 2.2 An Algorithm for the Adaptive EVI-Estimation -- 3 A Small-Scale Simulation Study -- 4 A Case Study -- 5 Conclusions -- References -- Estimation of the Weibull Tail Coefficient Through the Power Mean-of-Order-p -- 1 A Brief Introduction -- 2 A Brief Motivation for the Need of EVT -- 3 A Brief Touch on Asymptotical EVT -- 4 Semi-parametric Estimation in SUE -- 4.1 A Class of GM EVI-Estimators -- 4.2 Semi-parametric Estimation of the WTC -- 4.3 Consistency of the WTC-Estimators -- 5 Finite Sample Behaviour with Simulated Data -- 6 Overall Conclusions -- References -- On the Maximum of a Bivariate Max-INAR(1) Process -- 1 Introduction -- 2 Stationarity of the Process.
3 Limiting Distribution of the Bivariate Maximum -- References -- The Performance of a Combined Distance Between Time Series -- 1 Introduction -- 2 Methodology -- 2.1 UCR Repository -- 2.2 Using the 1NN Classifier -- 2.3 Dissimilarity Measures -- 2.4 Evaluating the Classification Results -- 3 Data Analysis and Results -- 3.1 General Comparisons -- 3.2 COMB ``Wins'' and ``Looses'' Examples -- 4 Discussion and Future Research -- 5 Appendix: The Datasets -- References -- Zero-Distorted Generalized Geometric Distribution with Application to Time Series of Counts -- 1 Introduction -- 2 The Zero-Distorted Generalized Geometric Distribution -- 3 Estimators of ZDGG Distribution Parameters -- 3.1 Asymptotic Behaviour of the Estimators of (q,α) -- 3.2 Numerical Studies: Behaviour of Estimators in Moderate and Large Sample Sizes -- 4 The INARCH Model with Conditional ZDGG Distribution -- 4.1 Definition and First-order Stationarity -- 4.2 Real-Data Application: Number of New Hantavirus Infections Per Week in a German State -- 5 Conclusion -- References -- Uncovering Abnormal Water Consumption Patterns for Sustainability's Sake: A Statistical Approach -- 1 Introduction -- 2 Methodology -- 3 Data -- 4 Results -- 5 Conclusion -- References -- Modeling and Forecasting Wind Energy Production by Stochastic Differential Equations -- 1 Introduction -- 2 The Problem Under Study -- 3 Modeling via SDEs -- 3.1 Parameter Estimation -- 3.2 Analysis of the Residuals -- 4 Forecasting -- 5 Final Comments -- References -- Intensity-Dependent Point Processes -- 1 Introduction -- 2 Intensity-Dependent Processes -- 2.1 Geostatistical Model for Preferential Sampling -- 2.2 Log-Intensity Marked Cox Processes -- 2.3 Geostatistical Model for Preferential Sampling Versus Log-Intensity Marked Cox Processes -- 3 Test to Detect Preferential Sampling or Intensity-Dependent Marks. 3.1 Nearest Neighbour Test -- 3.2 Schlather Test -- 3.3 Envelope Tests -- 4 Data Example -- 4.1 Data -- 4.2 Application of Nearest Neighbour Test to BSF catches -- 4.3 Application of Schlater Test to BSF catches -- 5 Final Remarks and Future Work -- References -- Geostatistical Sampling Designs Under Preferential Sampling for Black Scabbardfish -- 1 Introduction -- 2 Methods -- 2.1 Geostatistical Model Under Preferential Sampling -- 2.2 Sampling Designs -- 3 Results -- 3.1 BSF Data -- 3.2 Model Fitting Under Preferential Sampling -- 3.3 Sampling Designs for Black Scabbardfish -- 4 Discussion -- References -- Modeling Residential Adoption of Solar Photovoltaic Systems -- 1 Introduction -- 1.1 Decision-Making in PV Technology Adoption -- 2 Material and Methods -- 2.1 Data Characterization and Preprocessing -- 2.2 Data Modeling -- 3 Results -- 3.1 Exploratory Analysis -- 3.2 Models -- 4 Conclusions and Discussion -- References -- Comparison of Semiparametric Approaches to Two-Way ANOVA in the Presence of Heteroscedasticity -- 1 Introduction -- 2 Statistical Model and Hypotheses -- 3 Compared Methods -- 3.1 Wald-Type Statistic (WTS) -- 3.2 ANOVA-Type Statistic (ATS) -- 3.3 Permutation Tests -- 4 Simulation -- 5 Results -- 5.1 Homoscedastic Versus Heteroscedastic Settings -- 5.2 Effect Size -- 5.3 Model Effect -- 5.4 Sample Size Effect -- 5.5 Ties Effect -- 6 Conclusions -- References -- Some Determinants for Road Accidents Severity in the District of Setúbal -- 1 Introduction -- 2 Methods -- 2.1 Study Area -- 2.2 Data -- 2.3 Statistical Analysis -- 3 Results -- 4 Conclusions -- References -- Impact of Misclassification and Imperfect Serological Tests in Association Analyses of ME/CFS Applied to COVID-19 Data -- 1 Background -- 2 Simulation Study -- 2.1 Mathematical Formulation of the Problem -- 2.2 Parameterisation Using Real-Word Data. 2.3 Simulation Structure -- 3 Simulation Results -- 4 Discussion -- References -- Identification of Antibody Responses Predictive of Protection Against Clinical Malaria -- 1 Introduction -- 2 Materials and Methods -- 2.1 KEN Dataset -- 2.2 Measuring Association -- 2.3 Predictive Methodologies -- 2.4 Predictive Accuracy -- 2.5 Pipeline -- 3 Results -- 4 Discussion -- 5 Concluding Remarks and Future Work -- References -- Statistical Challenges in Mutational Signature Analyses of Cancer Sequencing Data -- 1 Introduction -- 1.1 Modelling Framework -- 1.2 Mathematical Approaches to Mutational Signatures -- 2 Challenges in Constructing M -- 2.1 Challenge 1: Accounting for Bias and Variance in M -- 2.2 Challenge 2: Recognising Intra-Tumour Heterogeneity -- 2.3 Challenge 3: Accounting for Opportunities -- 2.4 Challenge 4: Going Beyond the 96 Categories -- 3 Challenges Addressed with Bayesian Nonparametrics -- 3.1 Challenge 5: Uncertainty in the Number of Signatures -- 3.2 Challenge 6: Uncertainty Around the Signatures -- 3.3 Challenge 7: Sample Size Calculations -- 4 Challenges Requiring a New Modelling Approach -- 4.1 Challenge 8: Uncertainty Quantification Around Exposures -- 4.2 Challenge 9: Obtaining Separated Signatures -- 4.3 Challenge 10: Partial Information About the Signatures -- 5 Conclusions -- References -- PCR Duplicate Proportion Estimation and Consequences for DNA Copy Number Calculations -- 1 Duplicate Sequencing Reads -- 1.1 An Example Data Set -- 2 Approaches to Separating Out the Duplicate Types -- 3 A Likelihood Approach Based on Allele Patterns at Heterozygous Loci -- 3.1 A Simple Approach Using Only Pairs of Duplicates -- 3.2 A Likelihood Approach for Pairs of Duplicates -- 3.3 The Full Model -- 3.4 Application to Our Example Data -- 4 Effects on the Estimation of DNA Copy Number -- 5 The Estimation of Mitochondrial DNA Copy Number. 5.1 PCAWG Copy Number -- 5.2 An Approach to Correct the Estimate of mtDNA Copy Number -- 5.3 Example -- 6 Conclusions -- References -- A Retrospective Study on Obstructive Sleep Apnea -- 1 Introduction -- 2 Data -- 3 Statistical Analysis -- 3.1 Association Analysis -- 3.2 Agreement Analysis -- 4 Results -- 5 Conclusions -- References -- Censored Multivariate Linear Regression Model -- 1 Introduction -- 2 Censored Multivariate Linear Regression -- 2.1 The Multivariate Linear Regression Model -- 2.2 The Censored Multivariate Linear Regression Model -- 3 Estimation of CMLR Model -- 3.1 EM Algorithm for Multivariate Data -- 3.2 Data Augmentation Algorithm -- 3.3 Gibbs Sampler with Data Augmentation Algorithm -- 4 Simulation Study -- 5 Final Remarks -- References -- A Methodology to Reveal Terrain Effects from Wind Farm SCADA Data Using a Wind Signature Concept -- 1 Introduction -- 2 Background -- 2.1 SCADA Data -- 2.2 Data Pre-processing/Cleansing -- 2.3 Variables of Interest -- 2.4 Frequency Wind Roses -- 2.5 Fluctuations in the Direction Signal -- 3 The Proposed Methodology -- 3.1 Overview -- 3.2 Steady Wind -- 3.3 Harvesting and Selection of Time Bands -- 3.4 Time Band Significance Index -- 3.5 Instantaneous Wind Signatures -- 4 Case Study -- 5 Conclusions -- References -- A Robust Version of the FGLS Estimator for Panel Data -- 1 Introduction and Preliminaries -- 1.1 Panel Data Model (PDM) -- 1.2 Robust Methods for PDM -- 2 The FGLS Estimator -- 3 A Robust FGLS Estimator -- 4 Illustration with the Grunfeld Data -- 4.1 Model Parameters Estimates -- 5 Concluding Remarks and Future Work -- References -- The Extended Chen-Poisson Marginal Rate Model for Recurrent Gap Time Data -- 1 Introduction -- 2 Formulation of the ECP Marginal Rate Model -- 3 Statistical Inference -- 4 Simulation Study -- 5 Application to Bowel Motility Data -- 6 Concluding Remarks. References. |
| Record Nr. | UNISA-996499867903316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Reflections on the foundations of probability and statistics : essays in honor of Teddy Seidenfeld / / edited by Thomas Augustin, Fabio Gagliardi Cozman, Gregory Wheeler
| Reflections on the foundations of probability and statistics : essays in honor of Teddy Seidenfeld / / edited by Thomas Augustin, Fabio Gagliardi Cozman, Gregory Wheeler |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (350 pages) |
| Disciplina | 780 |
| Collana | Theory and Decision Library A:, Rational Choice in Practical Philosophy and Philosophy of Science |
| Soggetto topico |
Mathematics
Probabilitats Estadística matemàtica |
| Soggetto genere / forma |
Homenatges
Llibres electrònics |
| ISBN | 3-031-15436-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | An Interview with Teddy Seidenfeld -- The Value Provided by a Scientific Explanation -- A Gentle Approach to Imprecise Probability -- Foundations For Temporal Reasoning Using Lower Previsions Without A Possibility Space -- On the Equivalence of Normal and Extensive Form Representations of Games -- Dilation and Informativeness -- Playing with Sets of Lexicographic Probabilities and Sets of Desirable Gambles -- How to Assess Coherent Beliefs: A Comparison of Different Notions of Coherence in Dempster-Shafer Theory of Evidence -- Expected Utility in 3D -- On the Normative Status of Mixed Strategies -- On a Notion of Independence Proposed by Teddy Seidenfeld -- Coherent Choice Functions without Archimedeanity -- Quantifying Degrees of E-admissibility in Decision Making with Imprecise Probabilities. |
| Record Nr. | UNINA-9910644257103321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Reflections on the foundations of probability and statistics : essays in honor of Teddy Seidenfeld / / edited by Thomas Augustin, Fabio Gagliardi Cozman, Gregory Wheeler
| Reflections on the foundations of probability and statistics : essays in honor of Teddy Seidenfeld / / edited by Thomas Augustin, Fabio Gagliardi Cozman, Gregory Wheeler |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (350 pages) |
| Disciplina | 780 |
| Collana | Theory and Decision Library A:, Rational Choice in Practical Philosophy and Philosophy of Science |
| Soggetto topico |
Mathematics
Probabilitats Estadística matemàtica |
| Soggetto genere / forma |
Homenatges
Llibres electrònics |
| ISBN | 3-031-15436-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | An Interview with Teddy Seidenfeld -- The Value Provided by a Scientific Explanation -- A Gentle Approach to Imprecise Probability -- Foundations For Temporal Reasoning Using Lower Previsions Without A Possibility Space -- On the Equivalence of Normal and Extensive Form Representations of Games -- Dilation and Informativeness -- Playing with Sets of Lexicographic Probabilities and Sets of Desirable Gambles -- How to Assess Coherent Beliefs: A Comparison of Different Notions of Coherence in Dempster-Shafer Theory of Evidence -- Expected Utility in 3D -- On the Normative Status of Mixed Strategies -- On a Notion of Independence Proposed by Teddy Seidenfeld -- Coherent Choice Functions without Archimedeanity -- Quantifying Degrees of E-admissibility in Decision Making with Imprecise Probabilities. |
| Record Nr. | UNISA-996508571803316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Research on Reasoning with Data and Statistical Thinking: International Perspectives [[electronic resource] /] / edited by Gail F. Burrill, Leandro de Oliveria Souza, Enriqueta Reston
| Research on Reasoning with Data and Statistical Thinking: International Perspectives [[electronic resource] /] / edited by Gail F. Burrill, Leandro de Oliveria Souza, Enriqueta Reston |
| Autore | Burrill Gail F |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (375 pages) |
| Disciplina | 001.422 |
| Altri autori (Persone) |
de Oliveria SouzaLeandro
RestonEnriqueta |
| Collana | Advances in Mathematics Education |
| Soggetto topico |
Mathematics - Study and teaching
Teachers - Training of Study Skills Mathematics Education Teaching and Teacher Education Study and Learning Skills.5 Estadística matemàtica Ensenyament de la matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-29459-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Part I. Statistics Education Across the World -- Chapter 2. An International Look at the Status of Statistics Education -- Chapter 3. Perspectives on Statistics Education in Seven Countries -- Chapter 4. The Brazilian National Curricular Guidance and Statistics Education -- Chapter 5. Statistics and Probability Education in Germany -- Chapter 6. New Zealand Statistics Curriculum -- Chapter 7. Statistics Education in the Philippines: Curricular Context and Challenges of Implementation -- Chapter 8. Statistics and Probability in the Curriculum in South Africa -- Chapter 9. Statistics in the School Level in Turkey -- Chapter 10. United States Statistics Curriculum.-Part II. Data and Young Learners -- Chapter 11 -- Elementary Students’ Responses to Quantitative Data -- Chapter 12. Reading and Interpreting Distributions of Numerical Data in Primary School -- Chapter 13.Young Learners Experiencing the World through Data Modeling -- Part III. Data and Simulation to Support Understanding -- Chapter 14. Investigating Mathematics Teacher Educators' Conceptions and Criteria for an Informal Line of Best Fit -- Chapter 15. Introducing Density Histograms to Grades 10 and 12 Students: Design and Try Out of an Intervention Inspired by Embodied Instrumentation -- Chapter 16. Margin of Error: Connecting Chance to Plausible -- Chapter 17. The Mystery of the Black Box: An Experience of Informal Inferential Reasoning -- Part IV. Data and Society -- Chapter 18. Critical Citizenship in Statistics Teacher Education -- Chapter 19. Toward Statistical Literacy to Critically Approach Big Data in Mathematics Education -- Chapter 20. Interdisciplinary Data Workshops: Combining Statistical Consultancy Training with Practitioner Data Literacy -- Part V. Statistical Learning, Reasoning and Attitudes -- Chapter 21. Distinctive Aspects of Reasoning in Statistics and Mathematics: Implications for Classroom Arguments -- Chapter 22. Teaching Statistics and Sustainable Learning -- Chapter 23. How Students’ Statistics Beliefs Influence their Attitudes -- Chapter 24. Algebraization Levels of Statistical Tables in Secondary Textbooks. |
| Record Nr. | UNINA-9910731458703321 |
Burrill Gail F
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Research Papers in Statistical Inference for Time Series and Related Models [[electronic resource] ] : Essays in Honor of Masanobu Taniguchi / / edited by Yan Liu, Junichi Hirukawa, Yoshihide Kakizawa
| Research Papers in Statistical Inference for Time Series and Related Models [[electronic resource] ] : Essays in Honor of Masanobu Taniguchi / / edited by Yan Liu, Junichi Hirukawa, Yoshihide Kakizawa |
| Autore | Liu Yan |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (591 pages) |
| Disciplina | 519.55 |
| Altri autori (Persone) |
HirukawaJunichi
KakizawaYoshihide |
| Soggetto topico |
Time-series analysis
Mathematical statistics Nonparametric statistics Time Series Analysis Parametric Inference Non-parametric Inference Mathematical Statistics Estadística matemàtica Anàlisi de sèries temporals |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-9908-03-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Frequency domain empirical likelihood method for infinite variance models -- Chapter 2. Diagnostic testing for time series -- Chapter 3. Statistical Inference for Glaucoma Detection -- Chapter 4. On Hysteretic Vector Autoregressive Model with Applications -- Chapter 5. Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression -- Chapter 6. Exact topological inference on resting-state brain networks -- Chapter 7. An Introduction to Geostatistics -- Chapter 8. Relevant change points in high dimensional time series -- Chapter 9. Adaptiveness of the empirical distribution of residuals in semi-parametric conditional location scale models -- Chapter 10. Standard testing procedures for white noise and heteroskedasticity -- Chapter 11. Estimation of Trigonometric Moments for Circular Binary Series -- Chapter 12. Time series analysis with unsupervised learning -- Chapter 13. Recovering the market volatility shocks in high-dimensional time series -- Chapter 14. Asymptotic properties of mildly explosive processes with locally stationary disturbance -- Chapter 15. Multi-Asset Empirical Martingale Price Estimators for Financial Derivatives -- Chapter 16. Consistent Order Selection for ARFIMA Processes -- Chapter 17. Recursive asymmetric kernel density estimation for nonnegative data -- Chapter 18. Fitting an error distribution in some heteroscedastic time series models -- Chapter 19. Symbolic Interval-Valued Data Analysis for Time Series Based on Auto-Interval-Regressive Models -- Chapter 20. ROBUST LINEAR INTERPOLATION AND EXTRAPOLATION OF STATIONARY TIME SERIES -- Chapter 21. Non Gaussian models for fMRI data -- Chapter 22. Robust inference for ordinal response models -- Chapter 23. Change point problems for diffusion processes and time series models -- Chapter 24. Empirical likelihood approach for time series -- Chapter 25. Exploring the Dependence Structure Between Oscillatory Activities in Multivariate Time Series -- Chapter 26. Projection-based nonparametric goodness-of-fit testing with functional data. |
| Record Nr. | UNINA-9910728935303321 |
|
Liu Yan
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Sampling designs dependent on sample parameters of auxiliary variables / / Janusz L. Wywial
| Sampling designs dependent on sample parameters of auxiliary variables / / Janusz L. Wywial |
| Autore | Wywiał Janusz |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Berlin, Germany : , : Springer-Verlag, , [2021] |
| Descrizione fisica | 1 online resource (113 pages) |
| Disciplina | 519.52 |
| Collana | SpringerBriefs in Statistics |
| Soggetto topico |
Sampling (Statistics)
Mathematical statistics Mostreig (Estadística) Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-662-63413-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910495188603321 |
|
Wywiał Janusz
|
||
| Berlin, Germany : , : Springer-Verlag, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Sampling designs dependent on sample parameters of auxiliary variables / / Janusz L. Wywial
| Sampling designs dependent on sample parameters of auxiliary variables / / Janusz L. Wywial |
| Autore | Wywiał Janusz |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Berlin, Germany : , : Springer-Verlag, , [2021] |
| Descrizione fisica | 1 online resource (113 pages) |
| Disciplina | 519.52 |
| Collana | SpringerBriefs in Statistics |
| Soggetto topico |
Sampling (Statistics)
Mathematical statistics Mostreig (Estadística) Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-662-63413-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466387703316 |
|
Wywiał Janusz
|
||
| Berlin, Germany : , : Springer-Verlag, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Sampling, Approximation, and Signal Analysis [[electronic resource] ] : Harmonic Analysis in the Spirit of J. Rowland Higgins / / edited by Stephen D. Casey, M. Maurice Dodson, Paulo J. S. G. Ferreira, Ahmed Zayed
| Sampling, Approximation, and Signal Analysis [[electronic resource] ] : Harmonic Analysis in the Spirit of J. Rowland Higgins / / edited by Stephen D. Casey, M. Maurice Dodson, Paulo J. S. G. Ferreira, Ahmed Zayed |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2023 |
| Descrizione fisica | 1 online resource (XXXIV, 558 p. 35 illus., 31 illus. in color.) |
| Disciplina | 511.4 |
| Collana | Applied and Numerical Harmonic Analysis |
| Soggetto topico |
Approximation theory
Harmonic analysis Fourier analysis Signal processing Approximations and Expansions Abstract Harmonic Analysis Fourier Analysis Signal, Speech and Image Processing Mostreig (Estadística) Estadística matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-41130-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | PART I: Classical Sampling - Classical and approximate exponential sampling formula: their interconnections in uniform and Mellin–Lebesgue norms (Schmeisser) -- Asymptotic theorems for Durrmeyer sampling operators with respect to the L-norm (Vinti) -- On generalized Shannon sampling operators in the cosine operator function framework (Kivinukk) -- Bernstein spaces, sampling, and Riesz-Boas interpolation formulas in Mellin Analysis (Pesenson) -- The behavior of frequency band limited cardinal interpolants(Madych) -- The Balian-Low theorem for (Cq)-systems in shift-invariant spaces (owell) -- Whittaker - type derivative sampling and (p; q) - order weighted diffrential operator (Pogány) -- Shannon Sampling via Poisson, Cauchy, Jacobi and Levin (Casey) -- Part (II.) Theoretical Extensions - Schoenberg’s Theory of Totally Positive Functions and the Riemann Zeta Function (Gröchenig) -- Sampling via the Banach Gelfand Triple (Feichtinger) -- Part (III.) Frame Theory - A Survey of Fusion Frames in Hilbert Spaces (Köhldorfer) -- Frames of iterations and vector-valued model spaces (Cabrelli) -- A survey on frame representations and operator orbits (Christensen) -- Three proofs of the Benedetto–Fickus theorem (Mixon) -- Clifford Prolate SpheroidalWavefunctions and Associated Shift Frames (Lakey) -- Part (IV.) Applications - Power Aware Analog To Digital Converters (Mulleti) -- Quaternionic coupled fractional Fourier transform on Boehmians (Zayed) -- Sampling : Theory and Applications – A History of the SampTA Meetings (Casey) -- Accelerartion Algorithms for Iterative Methods (Marvasti). |
| Record Nr. | UNINA-9910799488903321 |
| Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical analysis of microbiome data / / Somnath Datta and Subharup Guha, editors
| Statistical analysis of microbiome data / / Somnath Datta and Subharup Guha, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (348 pages) |
| Disciplina | 579 |
| Collana | Frontiers in probability and the statistical sciences |
| Soggetto topico |
Microbiology - Statistical methods
Microbiologia Estadística matemàtica Microorganismes Genètica microbiana |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-73351-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Part I Preprocessing and Bioinformatics Pipelines -- Denoising Methods for Inferring Microbiome Community Content and Abundance -- 1 Introduction -- 2 Common Algorithmic Denoising Strategies -- 3 Model-Based Denoising -- 3.1 Hierarchical Divisive Clustering -- 3.2 Finite Mixture Model -- 3.3 Denoising Long-Read Technology -- 4 Model Assessment -- 4.1 With Known Truth -- 4.1.1 Accuracy in ASV Identification -- 4.1.2 Accuracy in Read Assignments -- 4.2 With Unknown Truth -- 4.2.1 Assessment with UMIs -- 4.2.2 Clustering Stability -- 5 Conclusions -- References -- Statistical and Computational Methods for Analysis of Shotgun Metagenomics Sequencing Data -- 1 Introduction -- 2 Methods for Species Identification and Quantification of Microorganisms -- 3 Metagenome Assembly and Applications -- 3.1 de Bruijn Assembly of a Single Genome -- 3.2 Modification for Metagenome and Metagenome-Assembled Genomes -- 3.3 Compacted de Bruijn Graph -- 4 Estimation of Growth Rates for Metagenome-Assembled Genomes (MAGs) -- 5 Methods for Identifying Biosynthetic Gene Clusters -- 5.1 A Hidden Markov Model-Based Approach -- 5.2 A Deep Learning Approach -- 5.3 BGC Identification Based on Metagenomic Data -- 6 Future Directions -- References -- Bioinformatics Pre-Processing of Microbiome Data with An Application to Metagenomic Forensics -- 1 Introduction -- 2 Bioinformatics Pipeline -- 2.1 Microbiome Data -- 2.2 Quality Control -- 2.3 Taxonomic Profiling -- 2.3.1 MetaPhlAn2 -- 2.3.2 Kraken2 -- 2.3.3 Kaiju -- 2.4 Computing facilities -- 3 Methodology -- 3.1 Pre-Processing and Feature Selection -- 3.2 Exploration of Candidate Classifiers -- 3.3 The Ensemble Classifier -- 3.4 Class Imbalance -- 3.5 Performance Measures -- 3.6 Data Analysis -- 4 Results -- 5 Discussion -- 6 Data Acknowledgement -- 7 Code Availability.
References -- Part II Exploratory Analyses of Microbial Communities -- Statistical Methods for Pairwise Comparison of Metagenomic Samples -- 1 Introduction -- 2 Microbial Community Comparison Methods Based on OTU Abundance Data -- 3 Microbial Community Comparison Measures Based on a Phylogenetic Tree -- 3.1 The Fst Statistic and Phylogenetic Test for Comparing Communities -- 3.2 UniFrac, W-UniFrac, VAW-UniFrac, and Generalized UniFrac for Comparing Microbial Communities -- 3.3 VAW-UniFrac for Comparing Communities -- 4 Alignment-Free Methods for the Comparison of Microbial Communities -- 5 A Tutorial on the Use of UniFrac Type and Alignment-Free Dissimilarity Measures for the Comparison of Metagenomic Samples -- 5.1 Analysis Steps for UniFrac, W-UniFrac, Generalized UniFrac, and VAW-UniFrac -- 5.2 Analysis Steps for the Comparison of Microbial Communities Based on Alignment-Free Methods -- 6 Discussion -- References -- Beta Diversity and Distance-Based Analysis of Microbiome Data -- 1 Introduction -- 2 Quantifying Dissimilarity: Common Beta Diversity Metrics -- 3 Ordination and Dimension Reduction -- 3.1 Principal Coordinates Analysis -- 3.2 Double Principal Coordinate Analysis -- 3.3 Biplots -- 3.4 Accounting for Compositionality -- 3.5 Model-Based Ordination Using Latent Variables -- 4 Distance-Based Hypothesis Testing -- 4.1 Permutation Tests -- 4.2 Kernel Machine Regression Tests -- 4.3 Sum of Powered Score Tests -- 4.4 Adaptive Tests -- 4.5 Comparison of Distance-Based Tests -- 5 Strengths, Weaknesses, and Future Directions -- References -- Part III Statistical Models and Inference -- Joint Models for Repeatedly Measured Compositional and Normally Distributed Outcomes -- 1 Introduction -- 2 Motivating Data -- 3 Statistical Models -- 3.1 The Multinomial Logistic Mixed Model (MLMM) -- 3.2 Dirichlet-Multinomial Mixed Model (DMMM). 3.3 Goodness of Fit -- 4 Simulation Studies -- 4.1 Simulation Setting -- 4.2 Simulation Results -- 5 Data Analysis -- 6 Discussion -- 7 Software -- Appendix -- References -- Statistical Methods for Feature Identification in Microbiome Studies -- 1 Introduction -- 2 Differential Abundance Analysis -- 2.1 Compositional Methods -- 2.2 Count-Based Methods -- 2.3 Additional Notes -- 3 Mediation Analysis -- 4 Feature Identification Adjusting for Confounding -- 4.1 Covariate Adjustment -- 4.2 Model-Based Standardization -- 5 Summary -- References -- Statistical Methods for Analyzing Tree-Structured Microbiome Data -- 1 Introduction -- 2 Modeling Multivariate Count Data -- 2.1 Dirichlet-Multinomial Model -- 2.2 Dirichlet-Tree Multinomial Model -- 2.3 Implementation and Illustration -- 3 Estimating Microbial Compositions -- 3.1 Empirical Bayes Normalization -- 3.2 Phylogeny-Aware Normalization -- 3.3 Statistical Analysis of Compositional Data -- 3.4 Implementation and Illustration -- 4 Regression with Compositional Predictors -- 4.1 Constrained Lasso and Log-Ratio Lasso -- 4.2 Subcomposition Selection -- 4.3 Phylogeny-Aware Subcomposition Selection -- 4.4 Linear Regression and Variable Fusion -- 5 Additional References -- 6 Discussion -- References -- A Log-Linear Model for Inference on Bias in Microbiome Studies -- 1 Introduction -- 2 Methods -- 2.1 The Brooks' Data -- 2.2 Setup and Estimation -- 2.3 Inference -- 2.4 Testability of the Hypothesis -- 2.4.1 Example: Testable Hypotheses for Main Effects -- 2.4.2 Example: Testable Hypotheses for Interaction Effects -- 3 Simulations -- 3.1 Main Effect Simulation -- 3.2 Interaction Effect Simulation Based on the Brooks Data -- 4 Results -- 4.1 Simulation Results -- 4.2 Do Interactions Between Taxa Affect Bias in the Brooks' Data? -- 4.3 Plate and Sample Type Effects in the Brooks' Data -- 5 Discussion -- Appendix. References -- Part IV Bayesian Methods -- Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data -- 1 Introduction -- 2 Methods -- 2.1 Dirichlet-Multinomial Regression Models for Compositional Data -- 2.2 Variable Selection Priors -- 2.3 Network Priors -- 2.3.1 Unknown G -- 2.4 Dirichlet-Tree Multinomial Models -- 2.5 Posterior Inference -- 3 Simulated Data -- 3.1 Simulation Study for DM Regression Models -- 3.2 DM Sensitivity Analysis -- 3.3 Simulation Study for DTM Regression Models -- 3.4 DTM Sensitivity Analysis -- 4 Applications -- 4.1 Multi-omics Microbiome Study-Pregnancy Initiative (MOMS-PI) -- 4.2 Gut Microbiome Study -- 5 Conclusion -- References -- A Bayesian Approach to Restoring the Duality Between Principal Components of a Distance Matrix and Operational Taxonomic Units in Microbiome Analyses -- 1 Introduction -- 1.1 Motivating Datasets -- 1.2 Nonlinear or Stochastic Distances -- 1.3 Limitations of SVD-Based Approaches -- 2 A Bayesian Formulation -- 2.1 Posterior Density -- 3 Model Sum of Squares and Biplots -- 4 Posterior Inference -- 4.1 Gibbs Sampler -- 4.2 Dimension Reduction: Skinny Bayesian Technique -- 4.2.1 Subsetted Data Matrix -- 4.2.2 Lower Dimensional Parameters and Induced Posterior -- 4.2.3 Faster Inference Procedure -- 4.3 Model Parameter Estimates -- 5 Simulation Study -- 5.1 Generation Strategy -- 6 Data Analysis -- 6.1 Tobacco Data -- 6.2 Subway Data -- 7 Data Acknowledgement -- 8 Discussion -- Supplementary Materials -- Appendix -- Proof of Lemma 1 -- References -- Part V Special Topics -- Tree Variable Selection for Paired Case-Control Studies with Application to Microbiome Data -- 1 Introduction -- 2 Gini Index -- 2.1 Simulation Analysis -- 3 Multivariate Gini Index -- 3.1 Conditional Gini Index -- 4 Variable Importance -- 5 Analysis of Obesity Using Microbiome Data. 6 Discussion -- Appendix -- References -- Networks for Compositional Data -- 1 Introduction -- 2 Methods -- 2.1 Learning Networks from Marginal Associations -- 2.1.1 ReBoot -- 2.1.2 SparCC -- 2.1.3 CCLasso -- 2.1.4 COAT -- 2.2 Learning Networks from Conditional Associations -- 2.2.1 SPIEC-EASI -- 2.2.2 gCoda -- 2.2.3 SPRING -- 3 Data-Generating Models -- 3.1 Null Models -- 3.2 Copula Models -- 3.3 Logistic-Normal Model -- 4 Results -- 4.1 Spurious (Partial) Correlations -- 4.2 Performance in Network Discovery -- 4.3 Case Studies in R -- 5 Future Directions -- References -- Index. |
| Record Nr. | UNINA-9910508445803321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical analysis of microbiome data / / Somnath Datta and Subharup Guha, editors
| Statistical analysis of microbiome data / / Somnath Datta and Subharup Guha, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (348 pages) |
| Disciplina | 579 |
| Collana | Frontiers in probability and the statistical sciences |
| Soggetto topico |
Microbiology - Statistical methods
Microbiologia Estadística matemàtica Microorganismes Genètica microbiana |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-73351-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Part I Preprocessing and Bioinformatics Pipelines -- Denoising Methods for Inferring Microbiome Community Content and Abundance -- 1 Introduction -- 2 Common Algorithmic Denoising Strategies -- 3 Model-Based Denoising -- 3.1 Hierarchical Divisive Clustering -- 3.2 Finite Mixture Model -- 3.3 Denoising Long-Read Technology -- 4 Model Assessment -- 4.1 With Known Truth -- 4.1.1 Accuracy in ASV Identification -- 4.1.2 Accuracy in Read Assignments -- 4.2 With Unknown Truth -- 4.2.1 Assessment with UMIs -- 4.2.2 Clustering Stability -- 5 Conclusions -- References -- Statistical and Computational Methods for Analysis of Shotgun Metagenomics Sequencing Data -- 1 Introduction -- 2 Methods for Species Identification and Quantification of Microorganisms -- 3 Metagenome Assembly and Applications -- 3.1 de Bruijn Assembly of a Single Genome -- 3.2 Modification for Metagenome and Metagenome-Assembled Genomes -- 3.3 Compacted de Bruijn Graph -- 4 Estimation of Growth Rates for Metagenome-Assembled Genomes (MAGs) -- 5 Methods for Identifying Biosynthetic Gene Clusters -- 5.1 A Hidden Markov Model-Based Approach -- 5.2 A Deep Learning Approach -- 5.3 BGC Identification Based on Metagenomic Data -- 6 Future Directions -- References -- Bioinformatics Pre-Processing of Microbiome Data with An Application to Metagenomic Forensics -- 1 Introduction -- 2 Bioinformatics Pipeline -- 2.1 Microbiome Data -- 2.2 Quality Control -- 2.3 Taxonomic Profiling -- 2.3.1 MetaPhlAn2 -- 2.3.2 Kraken2 -- 2.3.3 Kaiju -- 2.4 Computing facilities -- 3 Methodology -- 3.1 Pre-Processing and Feature Selection -- 3.2 Exploration of Candidate Classifiers -- 3.3 The Ensemble Classifier -- 3.4 Class Imbalance -- 3.5 Performance Measures -- 3.6 Data Analysis -- 4 Results -- 5 Discussion -- 6 Data Acknowledgement -- 7 Code Availability.
References -- Part II Exploratory Analyses of Microbial Communities -- Statistical Methods for Pairwise Comparison of Metagenomic Samples -- 1 Introduction -- 2 Microbial Community Comparison Methods Based on OTU Abundance Data -- 3 Microbial Community Comparison Measures Based on a Phylogenetic Tree -- 3.1 The Fst Statistic and Phylogenetic Test for Comparing Communities -- 3.2 UniFrac, W-UniFrac, VAW-UniFrac, and Generalized UniFrac for Comparing Microbial Communities -- 3.3 VAW-UniFrac for Comparing Communities -- 4 Alignment-Free Methods for the Comparison of Microbial Communities -- 5 A Tutorial on the Use of UniFrac Type and Alignment-Free Dissimilarity Measures for the Comparison of Metagenomic Samples -- 5.1 Analysis Steps for UniFrac, W-UniFrac, Generalized UniFrac, and VAW-UniFrac -- 5.2 Analysis Steps for the Comparison of Microbial Communities Based on Alignment-Free Methods -- 6 Discussion -- References -- Beta Diversity and Distance-Based Analysis of Microbiome Data -- 1 Introduction -- 2 Quantifying Dissimilarity: Common Beta Diversity Metrics -- 3 Ordination and Dimension Reduction -- 3.1 Principal Coordinates Analysis -- 3.2 Double Principal Coordinate Analysis -- 3.3 Biplots -- 3.4 Accounting for Compositionality -- 3.5 Model-Based Ordination Using Latent Variables -- 4 Distance-Based Hypothesis Testing -- 4.1 Permutation Tests -- 4.2 Kernel Machine Regression Tests -- 4.3 Sum of Powered Score Tests -- 4.4 Adaptive Tests -- 4.5 Comparison of Distance-Based Tests -- 5 Strengths, Weaknesses, and Future Directions -- References -- Part III Statistical Models and Inference -- Joint Models for Repeatedly Measured Compositional and Normally Distributed Outcomes -- 1 Introduction -- 2 Motivating Data -- 3 Statistical Models -- 3.1 The Multinomial Logistic Mixed Model (MLMM) -- 3.2 Dirichlet-Multinomial Mixed Model (DMMM). 3.3 Goodness of Fit -- 4 Simulation Studies -- 4.1 Simulation Setting -- 4.2 Simulation Results -- 5 Data Analysis -- 6 Discussion -- 7 Software -- Appendix -- References -- Statistical Methods for Feature Identification in Microbiome Studies -- 1 Introduction -- 2 Differential Abundance Analysis -- 2.1 Compositional Methods -- 2.2 Count-Based Methods -- 2.3 Additional Notes -- 3 Mediation Analysis -- 4 Feature Identification Adjusting for Confounding -- 4.1 Covariate Adjustment -- 4.2 Model-Based Standardization -- 5 Summary -- References -- Statistical Methods for Analyzing Tree-Structured Microbiome Data -- 1 Introduction -- 2 Modeling Multivariate Count Data -- 2.1 Dirichlet-Multinomial Model -- 2.2 Dirichlet-Tree Multinomial Model -- 2.3 Implementation and Illustration -- 3 Estimating Microbial Compositions -- 3.1 Empirical Bayes Normalization -- 3.2 Phylogeny-Aware Normalization -- 3.3 Statistical Analysis of Compositional Data -- 3.4 Implementation and Illustration -- 4 Regression with Compositional Predictors -- 4.1 Constrained Lasso and Log-Ratio Lasso -- 4.2 Subcomposition Selection -- 4.3 Phylogeny-Aware Subcomposition Selection -- 4.4 Linear Regression and Variable Fusion -- 5 Additional References -- 6 Discussion -- References -- A Log-Linear Model for Inference on Bias in Microbiome Studies -- 1 Introduction -- 2 Methods -- 2.1 The Brooks' Data -- 2.2 Setup and Estimation -- 2.3 Inference -- 2.4 Testability of the Hypothesis -- 2.4.1 Example: Testable Hypotheses for Main Effects -- 2.4.2 Example: Testable Hypotheses for Interaction Effects -- 3 Simulations -- 3.1 Main Effect Simulation -- 3.2 Interaction Effect Simulation Based on the Brooks Data -- 4 Results -- 4.1 Simulation Results -- 4.2 Do Interactions Between Taxa Affect Bias in the Brooks' Data? -- 4.3 Plate and Sample Type Effects in the Brooks' Data -- 5 Discussion -- Appendix. References -- Part IV Bayesian Methods -- Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data -- 1 Introduction -- 2 Methods -- 2.1 Dirichlet-Multinomial Regression Models for Compositional Data -- 2.2 Variable Selection Priors -- 2.3 Network Priors -- 2.3.1 Unknown G -- 2.4 Dirichlet-Tree Multinomial Models -- 2.5 Posterior Inference -- 3 Simulated Data -- 3.1 Simulation Study for DM Regression Models -- 3.2 DM Sensitivity Analysis -- 3.3 Simulation Study for DTM Regression Models -- 3.4 DTM Sensitivity Analysis -- 4 Applications -- 4.1 Multi-omics Microbiome Study-Pregnancy Initiative (MOMS-PI) -- 4.2 Gut Microbiome Study -- 5 Conclusion -- References -- A Bayesian Approach to Restoring the Duality Between Principal Components of a Distance Matrix and Operational Taxonomic Units in Microbiome Analyses -- 1 Introduction -- 1.1 Motivating Datasets -- 1.2 Nonlinear or Stochastic Distances -- 1.3 Limitations of SVD-Based Approaches -- 2 A Bayesian Formulation -- 2.1 Posterior Density -- 3 Model Sum of Squares and Biplots -- 4 Posterior Inference -- 4.1 Gibbs Sampler -- 4.2 Dimension Reduction: Skinny Bayesian Technique -- 4.2.1 Subsetted Data Matrix -- 4.2.2 Lower Dimensional Parameters and Induced Posterior -- 4.2.3 Faster Inference Procedure -- 4.3 Model Parameter Estimates -- 5 Simulation Study -- 5.1 Generation Strategy -- 6 Data Analysis -- 6.1 Tobacco Data -- 6.2 Subway Data -- 7 Data Acknowledgement -- 8 Discussion -- Supplementary Materials -- Appendix -- Proof of Lemma 1 -- References -- Part V Special Topics -- Tree Variable Selection for Paired Case-Control Studies with Application to Microbiome Data -- 1 Introduction -- 2 Gini Index -- 2.1 Simulation Analysis -- 3 Multivariate Gini Index -- 3.1 Conditional Gini Index -- 4 Variable Importance -- 5 Analysis of Obesity Using Microbiome Data. 6 Discussion -- Appendix -- References -- Networks for Compositional Data -- 1 Introduction -- 2 Methods -- 2.1 Learning Networks from Marginal Associations -- 2.1.1 ReBoot -- 2.1.2 SparCC -- 2.1.3 CCLasso -- 2.1.4 COAT -- 2.2 Learning Networks from Conditional Associations -- 2.2.1 SPIEC-EASI -- 2.2.2 gCoda -- 2.2.3 SPRING -- 3 Data-Generating Models -- 3.1 Null Models -- 3.2 Copula Models -- 3.3 Logistic-Normal Model -- 4 Results -- 4.1 Spurious (Partial) Correlations -- 4.2 Performance in Network Discovery -- 4.3 Case Studies in R -- 5 Future Directions -- References -- Index. |
| Record Nr. | UNISA-996466407003316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Soggetto topico
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Estadística matemàtica
(152)
- Mathematical statistics (58)
- Statistics (27)
- Biometry (17)
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