Reliability and Statistics in Transportation and Communication : Selected Papers from the 17th International Conference on Reliability and Statistics in Transportation and Communication, RelStat’17, 18-21 October, 2017, Riga, Latvia / / edited by Igor Kabashkin, Irina Yatskiv, Olegas Prentkovskis |
Edizione | [1st ed. 2018.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
Descrizione fisica | 1 online resource (XV, 680 p. 211 illus.) |
Disciplina | 380.5 |
Collana | Lecture Notes in Networks and Systems |
Soggetto topico |
Computational complexity
Statistics Electrical engineering Transportation engineering Traffic engineering Complexity Applied Statistics Communications Engineering, Networks Transportation Technology and Traffic Engineering |
ISBN | 3-319-74454-2 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910299581703321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
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Lo trovi qui: Univ. Federico II | ||
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Selected Contributions on Statistics and Data Science in Latin America : 33 FNE and 13 CLATSE, 2018, Guadalajara, Mexico, October 1−5 / / edited by Isadora Antoniano-Villalobos, Ramsés H. Mena, Manuel Mendoza, Lizbeth Naranjo, Luis E. Nieto-Barajas |
Edizione | [1st ed. 2019.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
Descrizione fisica | 1 online resource (VIII, 154 p. 46 illus., 36 illus. in color.) |
Disciplina | 519.233 |
Collana | Springer Proceedings in Mathematics & Statistics |
Soggetto topico |
Markov processes
Statistics Quantitative research Big data Markov Process Applied Statistics Bayesian Inference Data Analysis and Big Data Big Data |
ISBN | 3-030-31551-7 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Anzarut, M., González, L. F., and Ortiz, M. T: A Heavy-tailed Multilevel Mixture Model for the Quick Count in the Mexican Elections of 2018 -- Baltazar, F. and Esparza, L. J. R: Bayesian estimation for the Markov-Modulated Diffusion Risk Model -- Sergio, A. B., Johny, J. P., Ana, B. N., and Purificaci´on, G: Meta-analysis in DTA with hierarchical models Bivariate and HSROC: Simulation study -- Coen, A. and Chaparro, B. G: Compound Dirichlet Processes -- Guti´errez, E. and Walker, S. G: An efficient method to determine the degree of overlap of two multivariate distributions -- Mart´ınez, A. F.: Clustering via non-symmetric partition distributions -- Naranjo, L., Fuentes, R., and P´erez, C, J: A Flexible Replication-Based Classification Approach for Parkinson’s Disease Detection by Using Voice Recordings -- Novoa, F., Espinoza, S. C., P´erez, A. C., and Duque, I. H: Calibration of population growth mathematical models by using time series -- Castro, E. P., Jaimes, F. G., Rodr´ıguez, E. B., Carreto, R. R., Roque, R. L., Leyva, V. V: Impact of the Red Code process using structural equation models -- Esparza, L. J. R.: On a construction of stationary processes via Bilateral Matrix-Exponential distributions -- Richards, E. I. V., Gallagher, E., and Su´arez, P: BoostNet: Bootstrapping detection of socialbots, and a case study from Guatemala. |
Record Nr. | UNINA-9910360853903321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
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Lo trovi qui: Univ. Federico II | ||
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SIR - Model Supported by a New Density [[electronic resource] ] : Action Document for an Adapted COVID - Management / / by Marcus Hellwig |
Autore | Hellwig Marcus |
Edizione | [1st ed. 2022.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
Descrizione fisica | 1 online resource (73 pages) |
Disciplina | 614.592414 |
Collana | Springer essentials |
Soggetto topico |
Statistics
Public health Biometry Probabilities Mathematical statistics Virology Applied Statistics Public Health Biostatistics Applied Probability Mathematical Statistics Epidemiologia COVID-19 |
Soggetto genere / forma | Llibres electrònics |
ISBN | 3-031-05273-0 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Occasion -- Objectives -- SIR model as the basis for a probabilistic model -- Preventive consideration using probabilistic SIR modelling -- The “infection curve” I (t) is replaced by the inclined, steep Eqb density function -- Events and findings from the recent past -- Ways out of symmetry, union with asymmetry -- Random scatter areas of the NV and the Eqb -- Presentation of the Equibalance Distribution, Eqb -- Infection management in relation to the course of incidence. |
Record Nr. | UNISA-996479367603316 |
Hellwig Marcus
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
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Lo trovi qui: Univ. di Salerno | ||
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SIR - Model Supported by a New Density : Action Document for an Adapted COVID - Management / / by Marcus Hellwig |
Autore | Hellwig Marcus |
Edizione | [1st ed. 2022.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
Descrizione fisica | 1 online resource (73 pages) |
Disciplina | 614.592414 |
Collana | Springer essentials |
Soggetto topico |
Statistics
Public health Biometry Probabilities Mathematical statistics Virology Applied Statistics Public Health Biostatistics Applied Probability Mathematical Statistics Epidemiologia COVID-19 |
Soggetto genere / forma | Llibres electrònics |
ISBN | 3-031-05273-0 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Occasion -- Objectives -- SIR model as the basis for a probabilistic model -- Preventive consideration using probabilistic SIR modelling -- The “infection curve” I (t) is replaced by the inclined, steep Eqb density function -- Events and findings from the recent past -- Ways out of symmetry, union with asymmetry -- Random scatter areas of the NV and the Eqb -- Presentation of the Equibalance Distribution, Eqb -- Infection management in relation to the course of incidence. |
Record Nr. | UNINA-9910574857303321 |
Hellwig Marcus
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
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Lo trovi qui: Univ. Federico II | ||
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Statistical Inference Based on Kernel Distribution Function Estimators / / by Rizky Reza Fauzi, Yoshihiko Maesono |
Autore | Fauzi Rizky Reza |
Edizione | [1st ed. 2023.] |
Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
Descrizione fisica | 1 online resource (103 pages) |
Disciplina | 519.5 |
Altri autori (Persone) | MaesonoYoshihiko |
Collana | JSS Research Series in Statistics |
Soggetto topico |
Statistics
Nonparametric statistics Mathematical statistics Statistical Theory and Methods Applied Statistics Non-parametric Inference Mathematical Statistics Estadística matemàtica Funcions de Kernel |
Soggetto genere / forma | Llibres electrònics |
Soggetto non controllato | Mathematics |
ISBN | 981-9918-62-6 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Kernel density estimator -- Kernel distribution estimator -- Quantile estimation -- Nonparametric tests -- Mean residual life estimator. |
Record Nr. | UNINA-9910728952603321 |
Fauzi Rizky Reza
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
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Lo trovi qui: Univ. Federico II | ||
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Statistical Learning of Complex Data / / edited by Francesca Greselin, Laura Deldossi, Luca Bagnato, Maurizio Vichi |
Edizione | [1st ed. 2019.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
Descrizione fisica | 1 online resource (XIII, 201 p. 37 illus., 11 illus. in color.) |
Disciplina | 519.5 |
Collana | Studies in Classification, Data Analysis, and Knowledge Organization |
Soggetto topico |
Statistics
Data mining Big data Statistical Theory and Methods Statistics and Computing/Statistics Programs Data Mining and Knowledge Discovery Applied Statistics Big Data/Analytics |
ISBN | 3-030-21140-1 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Preface -- Contributors -- Part I Clustering and Classification -- 1.1 Cluster Weighted Beta Regression: a simulation study -- 1.2 Detecting wine adulterations employing robust mixture of Factor Analyzers -- 1.3 Simultaneous supervised and unsupervised classification modeling for assessing cluster analysis and improving results interpretability -- 1.4 A parametric version of probabilistic distance clustering -- 1.5 An overview on the URV Model-Based Approach to Cluster Mixed-Type Data -- Part II Exploratory Data Analysis -- 2.1 Preference Analysis of Architectural Facades by Multidimensional Scaling and Unfolding -- 2.2 Community Structure in Co-authorship Networks: the Case of Italian Statisticians -- 2.3 Analyzing Consumers’ Behaviour in Brand Switching -- 2.4 Evaluating the Quality of Data Imputation in Cardiovascular Risk studies Through the Dissimilarity Profile Analysis -- Part III Statistical Modeling -- 3.1 Measuring Economic Vulnerability: a Structural Equation Modeling Approach -- 3.2 Bayesian Inference for a Mixture Model on the Simplex -- 3.3 Stochastic Models for the Size Distribution of Italian Firms: A Proposal -- 3.4 Modeling Return to Education in Heterogeneous Populations. An application to Italy -- 3.5 Changes in Couples’ Bread-winning Patterns and Wife’s Economic Role in Japan from 1985 to 2015 -- 3.6 Weighted Optimization with Thresholding for Complete-Case Analysis -- Part IV Graphical Models -- 4.1 Measurement Error Correction by NonParametric Bayesian Networks: Application and Evaluation -- 4.2 Copula Grow-Shrink Algorithm for Structural Learning -- 4.3 Context-Specific Independencies Embedded in Chain Graph Models of Type I -- Part V Big Data Analysis -- 5.1 Big Data and Network Analysis: A combined Approach to Model Online News -- 5.2 Experimental Design Issues in Big Data. The Question of Bias. |
Record Nr. | UNINA-9910349337803321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
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Lo trovi qui: Univ. Federico II | ||
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Statistical Methods in Human Genetics [[electronic resource] /] / by Indranil Mukhopadhyay, Partha Pratim Majumder |
Autore | Mukhopadhyay Indranil |
Edizione | [1st ed. 2023.] |
Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
Descrizione fisica | 1 online resource (281 pages) |
Disciplina | 361 |
Collana | Indian Statistical Institute Series |
Soggetto topico |
Medical genetics
Bioinformatics Statistics Medical Genetics Statistical Theory and Methods Applied Statistics |
ISBN | 981-9932-20-3 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Introduction to analysis of human genetic data -- Basic understanding of single gene expression data -- Basic probability theory and inference -- Analysis of single gene expression data -- Analysis of gene expression data in a dependent set up -- Tying genomes with disease -- Some extensions of genetic association study -- Exploring multivariate data. |
Record Nr. | UNINA-9910747597303321 |
Mukhopadhyay Indranil
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
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Lo trovi qui: Univ. Federico II | ||
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Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications [[electronic resource] ] : Selected Contributions from SimStat 2019 and Invited Papers / / edited by Jürgen Pilz, Viatcheslav B. Melas, Arne Bathke |
Autore | Pilz Jürgen |
Edizione | [1st ed. 2023.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
Descrizione fisica | 1 online resource (265 pages) |
Disciplina | 519.57 |
Altri autori (Persone) |
MelasViatcheslav B
BathkeArne |
Collana | Contributions to Statistics |
Soggetto topico |
Statistics
Mathematical statistics - Data processing Experimental design Machine learning Stochastic models Statistical Theory and Methods Statistics and Computing Design of Experiments Machine Learning Applied Statistics Stochastic Modelling in Statistics |
ISBN | 3-031-40055-0 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Intro -- Preface -- Contents -- Part I Invited Papers -- 1 Likelihood Ratios in Forensics: What They Are and What They Are Not -- 1.1 Introduction -- 1.2 Lindley's Likelihood Ratio (LLR) -- 1.2.1 Notations -- 1.2.2 A Frequentist Framework for Lindley's Likelihood Ratio (LLR) -- 1.3 Score-Based Likelihood Ratio (SLR) -- 1.3.1 The Expression of the SLR -- 1.3.2 The Glass Example -- 1.4 Discussion -- References -- 2 MANOVA for Large Number of Treatments -- 2.1 Introduction -- 2.2 Notations and Model Setup -- 2.3 Simulations -- 2.3.1 MANOVA Tests for Large g -- 2.3.2 Special Case: ANOVA for Large g -- 2.4 Discussion and Outlook -- References -- 3 Pollutant Dispersion Simulation by Means of a Stochastic Particle Model and a Dynamic Gaussian Plume Model -- 3.1 Introduction -- 3.2 Meteorological Monitoring Network -- 3.3 Wind Field Modeling -- 3.3.1 Mass Correction of the Wind Field -- 3.3.2 Plume Rise -- 3.4 Stochastic Particle Model -- 3.4.1 Deposition -- 3.4.2 Implementation -- 3.5 Dynamic Gaussian Plume Model -- 3.6 Implementation on the Server -- 3.7 A Real-World Example with Application to an Alpine Valley -- 3.8 Conclusions and Outlook -- References -- 4 On an Alternative Trigonometric Strategy for StatisticalModeling -- 4.1 Introduction -- 4.2 The Alternative Sine Distribution -- 4.2.1 Presentation -- 4.2.2 Moment Properties -- 4.2.3 Parametric Extensions -- 4.3 AS Generated Family -- 4.3.1 Definition -- 4.3.2 Series Expansions -- 4.3.3 Example: The ASE Exponential Distribution -- 4.3.4 Moment Properties -- 4.4 Application to a Famous Cancer Data -- 4.5 Conclusion -- References -- Part II Design of Experiments -- 5 Incremental Construction of Nested Designs Basedon Two-Level Fractional Factorial Designs -- 5.1 Introduction -- 5.2 Greedy Coffee-House Design -- 5.3 Two-Level Fractional Factorial Designs -- 5.3.1 Half Fractions: m=1.
5.3.2 Several Generators -- 5.3.2.1 Defining Relations -- 5.3.2.2 Resolution -- 5.3.2.3 Word Length Pattern -- 5.3.3 Minimum Size -- 5.4 Two-Level Factorial Designs and Error-Correcting Codes -- 5.4.1 Definitions and Properties -- 5.4.2 Examples -- 5.5 Maximin Distance Properties of Two-Level Factorial Designs -- 5.5.1 Neighbouring Pattern and Distant Site Pattern -- 5.5.2 Optimal Selection of Generators by Simulated Annealing -- 5.5.2.1 SA Algorithm for the Maximisation of ρH -- 5.6 Covering Properties of Two-Level Factorial Designs -- 5.6.1 Bounds on CRH(Xn) -- 5.6.2 Calculation of CRH(Xn) -- 5.6.2.1 Algorithmic Construction of a Lower Bound on CRH(Xn) -- 5.7 Greedy Constructions Based on Fractional Factorial Designs -- 5.7.1 Base Designs -- 5.7.2 Rescaled Designs -- 5.7.3 Projection Properties -- 5.8 Summary and Future Work -- Appendix -- References -- 6 A Study of L-Optimal Designs for the Two-Dimensional Exponential Model -- 6.1 Introduction -- 6.2 Equivalence Theorem for L-Optimal Designs -- 6.3 General Case -- 6.4 Excess and Saturated Designs -- References -- 7 Testing for Randomized Block Single-Case Designsby Combined Permutation Tests with Multivariate Mixed Data -- 7.1 Introduction -- 7.2 Randomized Block Single-Case Designs and NPC -- 7.3 Simulation Study -- 7.4 A Real Case Study -- 7.5 Conclusions -- References -- 8 Adaptive Design Criteria Motivated by a Plug-In Percentile Estimator -- 8.1 Introduction -- 8.2 Problem Formulation and Background -- 8.2.1 Problem Formulation -- 8.2.2 Background -- 8.3 The Plug-In Estimator -- 8.4 Adaptive ``Plug-In'' Criteria -- 8.4.1 Monte Carlo Approximation -- 8.4.2 Monte Carlo Approximation Assuming Independency -- 8.4.3 Assuming Independency and Neglecting Uncertainty -- 8.4.4 Using SUR Design Criterion for Exceedance Probability -- 8.5 Numerical Implementation -- 8.6 Numerical Study. 8.6.1 Comparison Study -- 8.6.2 Methodology -- 8.6.2.1 Case Studies -- 8.6.2.2 Performance Indicators -- 8.6.3 Numerical Results -- 8.6.3.1 Estimators Performance -- 8.6.3.2 Implementation -- 8.6.3.3 Criteria -- 8.7 Conclusions -- Appendix 1 -- Posterior Mean and Variance of f Under the Gaussian Process Assumption -- SUR Design Criteria for Exceedance Probability Estimation -- Appendix 2 -- References -- Part III Queueing and Inventory Analysis -- 9 On a Parametric Estimation for a Convolutionof Exponential Densities -- 9.1 Introduction -- 9.2 Convolution of the Exponential Densities -- 9.3 ML Estimation of the Parameters -- 9.4 Parameter's Estimation by the Moments' Method -- 9.5 Approximation of the Density -- 9.6 Experimental Study -- 9.7 Application to a Single Queueing System M/G/1/k -- 9.8 Conclusions -- References -- 10 Statistical Estimation with a Known Quantileand Its Application in a Modified ABC-XYZ Analysis -- 10.1 Introduction -- 10.2 Methods -- 10.2.1 Statistical Estimation with a Known Quantile -- 10.2.2 ABC-XYZ Analysis -- 10.3 ABC-XYZ Analysis Modified with a Known Quantile -- 10.4 Conclusions -- References -- Part IV Machine Learning and Applications -- 11 A Study of Design of Experiments and Machine Learning Methods to Improve Fault Detection Algorithms -- 11.1 Introduction -- 11.2 Design of Experiments and Machine Learning Modelling -- 11.3 Application to Fault Detection -- 11.3.1 Design of Experiments Step -- 11.3.2 Machine Learning Modelling Step -- 11.3.2.1 Refrigerant Undercharge: Fault Detection -- 11.3.2.2 Condenser Fouling: Fault Detection -- 11.4 Conclusions -- References -- 12 Microstructure Image Segmentation Using Patch-Based Clustering Approach -- 12.1 Introduction -- 12.2 Input Data -- 12.3 Previous Work -- 12.4 Grain Segmentation -- 12.4.1 Seeded Region Growing (SRG) -- 12.4.2 Image Denoising and Patch Determination. 12.4.3 Feature Extraction -- 12.4.4 Patch Clustering -- 12.4.5 Implementation -- 12.5 Results -- 12.6 Conclusion and Outlook -- References -- 13 Clustering and Symptom Analysis in Binary Datawith Application -- 13.1 Introduction -- 13.2 The Symptom Analysis -- 13.2.1 The Symptom and Syndrome Definition -- 13.2.2 Impulse Vector and Super-symptoms -- 13.2.3 Prefigurations of Super-symptom -- 13.2.4 The Super-symptom Recovery by Vector β -- 13.2.5 Clustering in Dichotomous Space and Symptom Analysis -- 13.3 The Medical Application of the Clustering and Symptom Analysis in Binary Data -- 13.3.1 Dataset -- 13.3.2 Result and Discussion -- 13.4 Conclusion -- References -- 14 Big Data for Credit Risk Analysis: Efficient Machine Learning Models Using PySpark -- 14.1 Introduction -- 14.2 Data Processing -- 14.2.1 Data Treatment -- 14.2.2 Data Storage and Distribution -- 14.2.3 Munge Data -- 14.2.4 Creating New Measures -- 14.2.5 Missing Values Imputation and Outliers Treatment -- 14.2.6 One-Hot Code and Dummy Variables -- 14.2.7 Final Dataset -- 14.3 Method and Models -- 14.3.1 Method -- 14.3.2 Model Building -- 14.4 Results and Credit Scorecard Conversion -- 14.5 Conclusion -- Appendix 1 -- Appendix 2 -- References. |
Record Nr. | UNINA-9910754092903321 |
Pilz Jürgen
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
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Lo trovi qui: Univ. Federico II | ||
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Statistical models and methods for data science / / Leonardo Grilli, Monia Lupparelli, Carla Rampichini, Emilia Rocco, Maurizio Vichi, editors |
Pubbl/distr/stampa | Cham : , : Springer, , [2023] |
Descrizione fisica | 1 online resource (viii, 188 pages) : illustrations |
Disciplina | 005.7 |
Collana | Studies in classification, data analysis, and knowledge organization |
Soggetto topico |
Mathematical statistics—Data processing
Quantitative research Machine learning Statistics Artificial intelligence—Data processing Statistics and Computing Data Analysis and Big Data Statistical Learning Statistical Theory and Methods Applied Statistics Data Science Dades massives Estadística matemàtica |
Soggetto genere / forma |
Congressos
Llibres electrònics |
ISBN |
3-031-30164-1
9783031301643 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Clustering financial time series by dependency -- The Homogeneity Index as a Measure of Interrater Agreement for Ratings on a Nominal Scale -- Hierarchical clustering of income data based on share densities -- Optimal Coding of High Cardinality Categorical Data in Machine Learning -- Bayesian Multivariate Analysis of Mixed data -- Marginals matrix under a generalized Mallows model based on the power divergence -- Time series clustering based on forecast distributions: an empirical analysis on production indices for construction -- Partial Reconstruction of Measures from Halfspace Depth -- Posterior Predictive Assessment of IRT Models via the Hellinger Distance: A Simulation Study -- Shapley Lorenz values for credit risk management -- A study of lack-of-fit diagnostics for models fit to cross-classified binary variables -- Robust Response Transformations for Generalized Additive Models via Additivity and Variance Stabilisation -- A Random-Coefficients Analysis with a Multivariate Random-Coefficients Linear Model -- Parsimonious mixtures of matrix-variate shifted exponential normal distributions. |
Record Nr. | UNINA-9910735775003321 |
Cham : , : Springer, , [2023] | ||
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Lo trovi qui: Univ. Federico II | ||
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Statistical Regression Modeling with R [[electronic resource] ] : Longitudinal and Multi-level Modeling / / by Ding-Geng (Din) Chen, Jenny K. Chen |
Autore | Chen Ding-Geng (Din) |
Edizione | [1st ed. 2021.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
Descrizione fisica | 1 online resource (239 pages) |
Disciplina | 519.536 |
Collana | Emerging Topics in Statistics and Biostatistics |
Soggetto topico |
Statistics
Programming languages (Electronic computers) Statistical Theory and Methods Applied Statistics Programming Language Anàlisi de regressió R (Llenguatge de programació) |
Soggetto genere / forma | Llibres electrònics |
ISBN | 3-030-67583-1 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | 1. Linear Regression -- 2. Introduction to Multi-Level Regression -- 3. Two-Level Multi-Level Modeling -- 4. Higher-Level Multi-Level Modeling -- 5. Longitudinal Data Analysis -- 6. Nonlinear Regression Modeling -- 7. Nonlinear Mixed-Effects Modeling -- 8. Generalized Linear Model -- 9. Generalized Multi-Level Model for Dichotomous Outcome -- 10. Generalized Multi-Level Model for Counts Outcome. |
Record Nr. | UNISA-996466552203316 |
Chen Ding-Geng (Din)
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
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Lo trovi qui: Univ. di Salerno | ||
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