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Biblioteca

MARC Lista (tabellare)
Fundamentals of statistical inference : what is the meaning of random error? / / Norbert Hirschauer, Sven Grüner, Oliver Musshoff
Fundamentals of statistical inference : what is the meaning of random error? / / Norbert Hirschauer, Sven Grüner, Oliver Musshoff
Autore Hirschauer Norbert
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (141 pages)
Disciplina 519.5
Collana SpringerBriefs in Applied Statistics and Econometrics
Soggetto topico Mathematical statistics
Probabilities
Estadística matemàtica
Probabilitats
Soggetto genere / forma Llibres electrònics
ISBN 3-030-99091-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910588596103321
Hirschauer Norbert  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others]
Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others]
Edizione [2nd ed.]
Pubbl/distr/stampa Berlin : , : Springer, , [2022]
Descrizione fisica 1 online resource (406 pages)
Disciplina 570.285
Collana Springer Handbooks of Computational Statistics
Soggetto topico Bioinformatics - Statistical methods
Bioinformatics
Bioinformàtica
Biologia computacional
Informàtica mèdica
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-662-65902-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Part I Single-Cell Analysis -- Computational and Statistical Methods for Single-Cell RNA Sequencing Data -- 1 Introduction -- 2 Data Preprocessing -- 2.1 Reads Mapping -- 2.2 Cell Barcodes Demultiplexing -- 2.3 UMI Collapsing -- 2.4 Cell Barcodes Selection -- 2.5 Summary -- 3 Data Normalization and Visualization -- 3.1 Background -- 3.2 Global Scaling Normalization for UMI Data -- 3.3 Probabilistic Model-Based Normalization for UMI Data -- 3.4 Dimension Reduction and Cell Clustering -- 4 Dropout Imputation -- 4.1 Background -- 4.2 Cell-Cell Similarity-Based Imputation -- 4.3 Gene-Gene Similarity-Based Imputation -- 4.4 Gene-Gene and Cell-Cell Similarity-Based Imputation -- 4.5 Deep Neural Network-Based Imputation -- 4.6 G2S3 -- 4.7 Methods Evaluation and Comparison -- 5 Differential Expression Analysis -- 5.1 Background -- 5.2 DE Methods Ignoring Subject Effects -- 5.3 DE Methods Considering Subject Effects -- 5.4 iDESC -- 5.5 DE Methods Evaluation and Comparison -- 5.5.1 Type I Error Comparison -- 5.5.2 Statistical Power Comparison -- 6 Concluding Remarks -- References -- Pre-processing, Dimension Reduction, and Clustering for Single-Cell RNA-seq Data -- 1 Introduction -- 2 Pre-processing of scRNA-seq Data -- 2.1 Removal of Batch Effects -- 2.2 Quality Control and Feature Selection -- 3 Dimension Reduction and Clustering -- 3.1 Dimension Reduction -- 3.2 Clustering -- 4 Conclusion -- References -- Integrative Analyses of Single-Cell Multi-Omics Data: A Review from a Statistical Perspective -- 1 Multi-Omics Data Profiled on Different Cells -- 2 Multi-Omics Data Profiled on the Same Single Cells -- 3 Challenges and Future Perspectives -- References -- Approaches to Marker Gene Identification from Single-Cell RNA-Sequencing Data -- 1 Introduction.
2 Marker Gene Selection Relies on Identifying Differentially Expressed Genes -- 3 Methods for Marker Gene Selection -- 3.1 Highest Expressed, Highest Variable -- 4 Supervised Methods -- 4.1 COMET -- 4.2 scGeneFit -- 5 Unsupervised Methods -- 5.1 Seurat -- 5.2 SC3 -- 5.3 SCMarker -- 5.4 scTIM -- 5.5 RankCorr -- 6 Discussion -- References -- Model-Based Clustering of Single-Cell Omics Data -- 1 Introduction -- 2 Single-Cell Transcriptomic Data Clustering -- 2.1 Single-Cell Transcriptomic Data Structure -- 2.2 DIMM-SC -- 2.3 Real Data Example -- 3 Population-Scale Single-Cell Transcriptomic Data Clustering -- 3.1 Population-Scale Single-Cell Transcriptomic Data Structure -- 3.2 BAMM-SC -- 3.3 Real Data Example -- 4 Single-Cell Multi-omics Data Clustering -- 4.1 CITE-seq Data Structure -- 4.2 BREM-SC -- 4.3 Real Data Example -- 5 Concluding Remarks -- References -- Deep Learning Methods for Single-Cell Omics Data -- 1 Introduction -- 2 Factor-Model-Based Deep Learning Approaches -- 2.1 Regularization and Priors on the Latent Factors -- 2.1.1 Gaussian Prior and Variational Inference -- 2.1.2 Adjust for Batch Effects and Confounding Covariates: Identifiability -- 2.1.3 Adjust for Batch Effects and Confounding Covariates: Implementation -- 2.1.4 Model Cell Population Structure in the Latent Space -- 2.2 Distributional Assumptions on Observed Data -- 2.2.1 Model Observed Data from scRNA-seq -- 2.2.2 Model Observed Data from scATAC-seq -- 2.2.3 Model Observed Data from Single-Cell Multiomics Technologies -- 2.3 Post-training Statistical Analyses -- 2.3.1 Denoising -- 2.3.2 Visualization, Clustering, and Trajectory Analysis -- 2.3.3 Prediction -- 3 Deep Learning Methods for Dimension Reduction -- 3.1 Construct the Loss Function -- 3.2 Extra Penalties and Regularization -- 4 Discussion -- References -- Part II Network Analysis.
Probabilistic Graphical Models for Gene Regulatory Networks -- 1 Introduction -- 2 Probabilistic Graphical Models -- 2.1 Graphical Model Basics -- 2.2 Markov Networks -- 2.3 Bayesian Networks -- 3 Classic Graphical Models for Reconstructing GRNs -- 3.1 Frequentist Approach -- 3.2 Bayesian Approach -- 3.3 Graphical Models Incorporating Prior Knowledge -- 4 Testing in Graphical Models -- 4.1 Parametric Test -- 4.2 Non-parametric Test for Global Graph Structure -- 5 Conclusion -- References -- Additive Conditional Independence for Large and Complex Biological Structures -- 1 Additive Conditional Independence (ACI) -- 1.1 Additive Reproducing Kernel Hilbert Spaces and Relevant Linear Operators -- 2 Variable Selection via ACI -- 2.1 Nonparametric Variable Selection -- 2.2 Penalized Least-Square Estimation with RKHS Operators -- 2.3 Matrix Representation of Operators and Algorithm -- 2.4 Data Example -- 3 Graphical Modeling Through ACI -- 3.1 Nonparametric Graphical Models -- 3.2 The Additive Conditional Covariance and Partial Correlation Operators -- 3.3 Operator-Level Estimation and the Algorithm -- 3.4 Data Examples -- References -- Integration of Boolean and Bayesian Networks -- 1 Introduction -- 2 Methods -- 2.1 s-p-scores Associated with Networks, SPAN -- 2.2 Network Learning -- 3 Results -- 3.1 An Example -- 3.2 Real Example -- 3.3 Complex Example -- 4 Discussion -- References -- Computational Methods for Identifying MicroRNA-Gene Regulatory Modules -- 1 Introduction -- 2 Identifying MiRNA-Gene Modules by Integrating Heterogeneous Data Sources -- 2.1 Bipartite Graph-Based Methods -- 2.2 Nonnegative Matrix Factorization Methods -- 2.3 Statistical Modeling Approaches -- 3 Evaluating the Performance of MiRNA-Gene Module Identification Methods -- 4 Discussion -- 5 Conclusions -- References -- Causal Inference in Biostatistics -- 1 Introduction.
1.1 Causation and Association -- 1.2 Two Conceptual Frameworks: Causal Effect and Causal Discovery -- 2 Causal Effect -- 2.1 Approaches to Causal Inference -- 2.2 Randomized Clinical Trials -- 2.2.1 Perfect Randomized Trials -- 2.2.2 Randomized Trials with Missing Data -- 2.2.3 Randomized Trials with Post-treatment Variables -- 2.3 Observational Studies -- 2.3.1 Unconfounded Treatment Assignment Conditional on Measured Covariates -- 2.3.2 Unmeasured Cofounding -- 3 Some Current Research Topics -- 3.1 Heterogenous Treatment Effect and Precision Medicine -- 3.2 Integrating Data from Randomized Controlled Trials and Observational Studies -- 3.3 Multiple Treatments -- 4 Software Appendix -- References -- Bayesian Balance Mediation Analysis in Microbiome Studies -- 1 Introduction -- 2 Bayesian Balance Mediation Model -- 2.1 Bayesian Balance Mediation Model with a Binary Treatment -- 2.2 Direct and Mediation Effect and Estimation Based on Predictive Posterior Distribution -- 3 MCMC Sampling -- 3.1 MCMC Sampling -- 3.2 Conditional Distributions -- 4 Applications to Real Data -- 4.1 Mediation Analysis at the Phylum Level -- 4.2 Analysis at the Order Level -- 5 Simulation Studies -- 5.1 Data Generation -- 5.2 Simulation Result -- 6 Discussion -- References -- Part III Systems Biology -- Identifying Genetic Loci Associated with Complex Trait Variability -- 1 Introduction -- 2 The Concept of vQTL -- 3 Statistical Methods for vQTL Mapping -- 3.1 Classical Nonparametric Tests -- 3.2 Regression-Based Methods -- 3.3 Two-Stage Methods -- 3.4 Quantile Integral Linear Model (QUAIL) -- 3.5 Dispersion Effects -- 4 Applications of vQTL -- 4.1 Examples of vQTL -- 4.2 Screening vQTL for Candidate Loci Involved in GxE Interaction -- 4.3 Variance Polygenic Score -- 4.4 Other Applications and Future Directions -- References.
Cell Type-Specific Analysis for High-throughput Data -- 1 Introduction -- 2 Cell Type Composition Estimation -- 3 Cell Type-Specific Differential Analysis -- 4 Step-by-step Tutorial -- References -- Recent Development of Computational Methods in the Field of Epitranscriptomics -- 1 Introduction -- 2 MeRIP-seq and Other Technologies for RNA Modification Profiling -- 3 Methods to Analyze MeRIP-seq Data -- 3.1 Count-Based Methods for Simple Study Designs -- 3.2 Methods Compatible with Confounding Factors -- 3.3 A Guide for RNA Differential Methylation Analysis Using RADAR -- 4 Web Resources on m6A Epitranscriptome -- 4.1 Web Servers with m6A Site Prediction -- 4.2 m6A Epitranscriptome Database -- 5 Discussion -- References -- Estimation of Tumor Immune Signatures from Transcriptomics Data -- 1 Introduction -- 2 Regression-Based Deconvolution Algorithms -- 2.1 Linear Least Squares Regression -- 2.2 Support Vector Regression -- 2.3 Other Deconvolution Methods -- 3 Gene Set Enrichment-Based Methods and Other Gene-Based Algorithms -- 3.1 Gene Set Enrichment Analysis (GSEA) -- 3.2 Single-Sample GSEA (ssGSEA) -- 4 Benchmark Studies -- 5 Discussions -- References -- Cross-Linking Mass Spectrometry Data Analysis -- 1 Introduction -- 1.1 Peptide Identification Based on Mass Spectrometry -- 1.2 Cross-Linked Peptides Identification -- 1.2.1 Cross-Linker Selection -- 1.2.2 Chemical Reaction -- 1.2.3 Enzyme Digestion -- 1.2.4 Enrichment of Cross-Linked Peptides -- 1.2.5 LC-MS and MS2 Acquisition -- 1.2.6 Data Interpretation -- 1.2.7 Quality Control -- 1.2.8 Downstream Applications -- 2 Non-cleavable Cross-Linking Methods -- 3 Cleavable Cross-Linking Methods -- 4 Time-Complexity Comparison Between Non-cleavable Methods and Cleavable Methods -- 5 False Discovery Rate in CL-MS -- 5.1 Target-Decoy Approach in Linear Peptides Identification.
5.2 TDA in Cross-Linked Peptides Identification.
Record Nr. UNINA-9910634054103321
Berlin : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others]
Handbook of statistical bioinformatics / / Henry Horng-Shing Lu [and three others]
Edizione [2nd ed.]
Pubbl/distr/stampa Berlin : , : Springer, , [2022]
Descrizione fisica 1 online resource (406 pages)
Disciplina 570.285
Collana Springer Handbooks of Computational Statistics
Soggetto topico Bioinformatics - Statistical methods
Bioinformatics
Bioinformàtica
Biologia computacional
Informàtica mèdica
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-662-65902-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Part I Single-Cell Analysis -- Computational and Statistical Methods for Single-Cell RNA Sequencing Data -- 1 Introduction -- 2 Data Preprocessing -- 2.1 Reads Mapping -- 2.2 Cell Barcodes Demultiplexing -- 2.3 UMI Collapsing -- 2.4 Cell Barcodes Selection -- 2.5 Summary -- 3 Data Normalization and Visualization -- 3.1 Background -- 3.2 Global Scaling Normalization for UMI Data -- 3.3 Probabilistic Model-Based Normalization for UMI Data -- 3.4 Dimension Reduction and Cell Clustering -- 4 Dropout Imputation -- 4.1 Background -- 4.2 Cell-Cell Similarity-Based Imputation -- 4.3 Gene-Gene Similarity-Based Imputation -- 4.4 Gene-Gene and Cell-Cell Similarity-Based Imputation -- 4.5 Deep Neural Network-Based Imputation -- 4.6 G2S3 -- 4.7 Methods Evaluation and Comparison -- 5 Differential Expression Analysis -- 5.1 Background -- 5.2 DE Methods Ignoring Subject Effects -- 5.3 DE Methods Considering Subject Effects -- 5.4 iDESC -- 5.5 DE Methods Evaluation and Comparison -- 5.5.1 Type I Error Comparison -- 5.5.2 Statistical Power Comparison -- 6 Concluding Remarks -- References -- Pre-processing, Dimension Reduction, and Clustering for Single-Cell RNA-seq Data -- 1 Introduction -- 2 Pre-processing of scRNA-seq Data -- 2.1 Removal of Batch Effects -- 2.2 Quality Control and Feature Selection -- 3 Dimension Reduction and Clustering -- 3.1 Dimension Reduction -- 3.2 Clustering -- 4 Conclusion -- References -- Integrative Analyses of Single-Cell Multi-Omics Data: A Review from a Statistical Perspective -- 1 Multi-Omics Data Profiled on Different Cells -- 2 Multi-Omics Data Profiled on the Same Single Cells -- 3 Challenges and Future Perspectives -- References -- Approaches to Marker Gene Identification from Single-Cell RNA-Sequencing Data -- 1 Introduction.
2 Marker Gene Selection Relies on Identifying Differentially Expressed Genes -- 3 Methods for Marker Gene Selection -- 3.1 Highest Expressed, Highest Variable -- 4 Supervised Methods -- 4.1 COMET -- 4.2 scGeneFit -- 5 Unsupervised Methods -- 5.1 Seurat -- 5.2 SC3 -- 5.3 SCMarker -- 5.4 scTIM -- 5.5 RankCorr -- 6 Discussion -- References -- Model-Based Clustering of Single-Cell Omics Data -- 1 Introduction -- 2 Single-Cell Transcriptomic Data Clustering -- 2.1 Single-Cell Transcriptomic Data Structure -- 2.2 DIMM-SC -- 2.3 Real Data Example -- 3 Population-Scale Single-Cell Transcriptomic Data Clustering -- 3.1 Population-Scale Single-Cell Transcriptomic Data Structure -- 3.2 BAMM-SC -- 3.3 Real Data Example -- 4 Single-Cell Multi-omics Data Clustering -- 4.1 CITE-seq Data Structure -- 4.2 BREM-SC -- 4.3 Real Data Example -- 5 Concluding Remarks -- References -- Deep Learning Methods for Single-Cell Omics Data -- 1 Introduction -- 2 Factor-Model-Based Deep Learning Approaches -- 2.1 Regularization and Priors on the Latent Factors -- 2.1.1 Gaussian Prior and Variational Inference -- 2.1.2 Adjust for Batch Effects and Confounding Covariates: Identifiability -- 2.1.3 Adjust for Batch Effects and Confounding Covariates: Implementation -- 2.1.4 Model Cell Population Structure in the Latent Space -- 2.2 Distributional Assumptions on Observed Data -- 2.2.1 Model Observed Data from scRNA-seq -- 2.2.2 Model Observed Data from scATAC-seq -- 2.2.3 Model Observed Data from Single-Cell Multiomics Technologies -- 2.3 Post-training Statistical Analyses -- 2.3.1 Denoising -- 2.3.2 Visualization, Clustering, and Trajectory Analysis -- 2.3.3 Prediction -- 3 Deep Learning Methods for Dimension Reduction -- 3.1 Construct the Loss Function -- 3.2 Extra Penalties and Regularization -- 4 Discussion -- References -- Part II Network Analysis.
Probabilistic Graphical Models for Gene Regulatory Networks -- 1 Introduction -- 2 Probabilistic Graphical Models -- 2.1 Graphical Model Basics -- 2.2 Markov Networks -- 2.3 Bayesian Networks -- 3 Classic Graphical Models for Reconstructing GRNs -- 3.1 Frequentist Approach -- 3.2 Bayesian Approach -- 3.3 Graphical Models Incorporating Prior Knowledge -- 4 Testing in Graphical Models -- 4.1 Parametric Test -- 4.2 Non-parametric Test for Global Graph Structure -- 5 Conclusion -- References -- Additive Conditional Independence for Large and Complex Biological Structures -- 1 Additive Conditional Independence (ACI) -- 1.1 Additive Reproducing Kernel Hilbert Spaces and Relevant Linear Operators -- 2 Variable Selection via ACI -- 2.1 Nonparametric Variable Selection -- 2.2 Penalized Least-Square Estimation with RKHS Operators -- 2.3 Matrix Representation of Operators and Algorithm -- 2.4 Data Example -- 3 Graphical Modeling Through ACI -- 3.1 Nonparametric Graphical Models -- 3.2 The Additive Conditional Covariance and Partial Correlation Operators -- 3.3 Operator-Level Estimation and the Algorithm -- 3.4 Data Examples -- References -- Integration of Boolean and Bayesian Networks -- 1 Introduction -- 2 Methods -- 2.1 s-p-scores Associated with Networks, SPAN -- 2.2 Network Learning -- 3 Results -- 3.1 An Example -- 3.2 Real Example -- 3.3 Complex Example -- 4 Discussion -- References -- Computational Methods for Identifying MicroRNA-Gene Regulatory Modules -- 1 Introduction -- 2 Identifying MiRNA-Gene Modules by Integrating Heterogeneous Data Sources -- 2.1 Bipartite Graph-Based Methods -- 2.2 Nonnegative Matrix Factorization Methods -- 2.3 Statistical Modeling Approaches -- 3 Evaluating the Performance of MiRNA-Gene Module Identification Methods -- 4 Discussion -- 5 Conclusions -- References -- Causal Inference in Biostatistics -- 1 Introduction.
1.1 Causation and Association -- 1.2 Two Conceptual Frameworks: Causal Effect and Causal Discovery -- 2 Causal Effect -- 2.1 Approaches to Causal Inference -- 2.2 Randomized Clinical Trials -- 2.2.1 Perfect Randomized Trials -- 2.2.2 Randomized Trials with Missing Data -- 2.2.3 Randomized Trials with Post-treatment Variables -- 2.3 Observational Studies -- 2.3.1 Unconfounded Treatment Assignment Conditional on Measured Covariates -- 2.3.2 Unmeasured Cofounding -- 3 Some Current Research Topics -- 3.1 Heterogenous Treatment Effect and Precision Medicine -- 3.2 Integrating Data from Randomized Controlled Trials and Observational Studies -- 3.3 Multiple Treatments -- 4 Software Appendix -- References -- Bayesian Balance Mediation Analysis in Microbiome Studies -- 1 Introduction -- 2 Bayesian Balance Mediation Model -- 2.1 Bayesian Balance Mediation Model with a Binary Treatment -- 2.2 Direct and Mediation Effect and Estimation Based on Predictive Posterior Distribution -- 3 MCMC Sampling -- 3.1 MCMC Sampling -- 3.2 Conditional Distributions -- 4 Applications to Real Data -- 4.1 Mediation Analysis at the Phylum Level -- 4.2 Analysis at the Order Level -- 5 Simulation Studies -- 5.1 Data Generation -- 5.2 Simulation Result -- 6 Discussion -- References -- Part III Systems Biology -- Identifying Genetic Loci Associated with Complex Trait Variability -- 1 Introduction -- 2 The Concept of vQTL -- 3 Statistical Methods for vQTL Mapping -- 3.1 Classical Nonparametric Tests -- 3.2 Regression-Based Methods -- 3.3 Two-Stage Methods -- 3.4 Quantile Integral Linear Model (QUAIL) -- 3.5 Dispersion Effects -- 4 Applications of vQTL -- 4.1 Examples of vQTL -- 4.2 Screening vQTL for Candidate Loci Involved in GxE Interaction -- 4.3 Variance Polygenic Score -- 4.4 Other Applications and Future Directions -- References.
Cell Type-Specific Analysis for High-throughput Data -- 1 Introduction -- 2 Cell Type Composition Estimation -- 3 Cell Type-Specific Differential Analysis -- 4 Step-by-step Tutorial -- References -- Recent Development of Computational Methods in the Field of Epitranscriptomics -- 1 Introduction -- 2 MeRIP-seq and Other Technologies for RNA Modification Profiling -- 3 Methods to Analyze MeRIP-seq Data -- 3.1 Count-Based Methods for Simple Study Designs -- 3.2 Methods Compatible with Confounding Factors -- 3.3 A Guide for RNA Differential Methylation Analysis Using RADAR -- 4 Web Resources on m6A Epitranscriptome -- 4.1 Web Servers with m6A Site Prediction -- 4.2 m6A Epitranscriptome Database -- 5 Discussion -- References -- Estimation of Tumor Immune Signatures from Transcriptomics Data -- 1 Introduction -- 2 Regression-Based Deconvolution Algorithms -- 2.1 Linear Least Squares Regression -- 2.2 Support Vector Regression -- 2.3 Other Deconvolution Methods -- 3 Gene Set Enrichment-Based Methods and Other Gene-Based Algorithms -- 3.1 Gene Set Enrichment Analysis (GSEA) -- 3.2 Single-Sample GSEA (ssGSEA) -- 4 Benchmark Studies -- 5 Discussions -- References -- Cross-Linking Mass Spectrometry Data Analysis -- 1 Introduction -- 1.1 Peptide Identification Based on Mass Spectrometry -- 1.2 Cross-Linked Peptides Identification -- 1.2.1 Cross-Linker Selection -- 1.2.2 Chemical Reaction -- 1.2.3 Enzyme Digestion -- 1.2.4 Enrichment of Cross-Linked Peptides -- 1.2.5 LC-MS and MS2 Acquisition -- 1.2.6 Data Interpretation -- 1.2.7 Quality Control -- 1.2.8 Downstream Applications -- 2 Non-cleavable Cross-Linking Methods -- 3 Cleavable Cross-Linking Methods -- 4 Time-Complexity Comparison Between Non-cleavable Methods and Cleavable Methods -- 5 False Discovery Rate in CL-MS -- 5.1 Target-Decoy Approach in Linear Peptides Identification.
5.2 TDA in Cross-Linked Peptides Identification.
Record Nr. UNISA-996503551803316
Berlin : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
How data quality affects our understanding of the earnings distribution / / Reza C. Daniels
How data quality affects our understanding of the earnings distribution / / Reza C. Daniels
Autore Daniels Reza Che
Pubbl/distr/stampa Singapore, : Springer Nature, 2022
Descrizione fisica 1 online resource (xx, 114 pages) : illustrations (some color)
Soggetto topico Income distribution - Statistical methods
Mathematical statistics
Distribució de la renda
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
Soggetto non controllato Methodology for Collecting
Estimating and Organizing Microeconomic Data
Survey Methods
Total Survey Error
Response Propensity Models
Multiple Imputation
Income Distribution
ISBN 981-19-3639-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys Questionnaire Design and Response Propensities for Labour Income Micro Data Univariate Multiple Imputation for Coarse Employee Income Data Conclusion: How Data Quality Affects our Understanding of the Earnings Distribution
Record Nr. UNISA-996483153703316
Daniels Reza Che  
Singapore, : Springer Nature, 2022
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
How data quality affects our understanding of the earnings distribution / / Reza C. Daniels
How data quality affects our understanding of the earnings distribution / / Reza C. Daniels
Autore Daniels Reza Che
Pubbl/distr/stampa Singapore, : Springer Nature, 2022
Descrizione fisica 1 online resource (xx, 114 pages) : illustrations (some color)
Soggetto topico Income distribution - Statistical methods
Mathematical statistics
Distribució de la renda
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
Soggetto non controllato Methodology for Collecting
Estimating and Organizing Microeconomic Data
Survey Methods
Total Survey Error
Response Propensity Models
Multiple Imputation
Income Distribution
ISBN 981-19-3639-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys Questionnaire Design and Response Propensities for Labour Income Micro Data Univariate Multiple Imputation for Coarse Employee Income Data Conclusion: How Data Quality Affects our Understanding of the Earnings Distribution
Record Nr. UNINA-9910580174103321
Daniels Reza Che  
Singapore, : Springer Nature, 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Important applications of the Behrens-Fisher statistic and the false discovery rate / / Tejas A. Desai
Important applications of the Behrens-Fisher statistic and the false discovery rate / / Tejas A. Desai
Autore Desai Tejas A.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (88 pages)
Disciplina 519.538
Collana SpringerBriefs in statistics
Soggetto topico Analysis of variance
Mathematical statistics
Anàlisi de variància
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 9783030998882
9783030998875
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996479367003316
Desai Tejas A.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Important applications of the Behrens-Fisher statistic and the false discovery rate / / Tejas A. Desai
Important applications of the Behrens-Fisher statistic and the false discovery rate / / Tejas A. Desai
Autore Desai Tejas A.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (88 pages)
Disciplina 519.538
Collana SpringerBriefs in statistics
Soggetto topico Analysis of variance
Mathematical statistics
Anàlisi de variància
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 9783030998882
9783030998875
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910578693803321
Desai Tejas A.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Interdisciplinary statistics in Mexico : AME virtual meeting, September 10-11, 2020, and 34 FNE, Acatlán, Mexico, September 22-24, 2021 / / Isadora Antoniano-Villalobos [and three others] editors
Interdisciplinary statistics in Mexico : AME virtual meeting, September 10-11, 2020, and 34 FNE, Acatlán, Mexico, September 22-24, 2021 / / Isadora Antoniano-Villalobos [and three others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (234 pages)
Disciplina 519.5
Collana Springer proceedings in mathematics & statistics
Soggetto topico Mathematical statistics
Statistics
Estadística matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-031-12778-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- A Methodological Proposal to Model and Evaluate the Complexity of the Mexican Geo-Electoral System -- 1 Introduction -- 2 Methodological Framework -- 2.1 Conceptualization of Electoral Complexity -- 2.2 Statistical Indicators for Quantifying Electoral Complexity -- 2.3 Data Transformation, Electoral Complexity Indices, and Stratification -- 3 ECI Construction and Stratification -- 3.1 Exploratory Analysis and PCA Implementation -- 3.2 Clustering Analysis and Stratification with K-Means -- 4 Electoral Complexity Ranking and Stratification Results -- 5 Conclusions -- References -- A Spatial Analysis of Drug Dealing in Mexico City -- 1 Introduction -- 2 Background -- 2.1 Drug Dealing as Part of Organized Crime -- 2.2 Research Approaches -- 3 Methodology -- 4 Results -- 4.1 Univariate Global and Local Moran's I -- 4.2 Bivariate Global and Local Moran's I -- 5 Discussion and Conclusions -- References -- Bayesian Hierarchical Multinomial Modeling of the 2021 Mexican Election Outcomes with Censored Samples -- 1 Introduction -- 2 Background -- 2.1 Estimation Methods in the 2021 Quick Count -- 2.2 NBM Model Background -- 2.3 The Bias Problem -- 3 The NBM Model -- 3.1 Specification -- 3.2 Consistency of Our Modeling Assumptions -- 3.3 Fitting Procedure -- 3.4 Model Adequacy -- 4 The 2021 Mexican Elections -- 4.1 Data and Sample Design -- 4.2 Results -- 5 Conclusions and Future Work -- References -- Assessing Hospitalization for SARS-CoV-2 Confirmed Cases by a Cross-Entropy Weighted Ensemble Classifier -- 1 Introduction -- 2 Material -- 2.1 Dataset Description -- 2.2 Data Analysis -- 3 Cross-entropy Weighted Ensemble Classifier -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Emotion Analysis to Identify Risk of Committing Suicide Using Statistical Learning -- 1 Background -- 2 Materials and Methods.
2.1 Emotion Mining -- 2.2 Statistical Learning Methods -- 2.3 Training and Test Datasets -- 3 Results -- 3.1 Supervised Models -- 3.2 Unsupervised Model -- 3.3 Model Comparison -- 3.4 Testing the Models with a New Test Set: COVID-19 -- 4 Conclusions -- References -- Characterizing Groups Using Latent Class Mixed Models: Antiretroviral Treatment Adherence Analysis -- 1 Introduction -- 2 Materials -- 2.1 Population -- 2.2 Data Collection -- 2.3 ART Adherence Definition -- 3 Latent Class Mixture Models -- 4 Results -- 4.1 Latent Classes from ART Adherence -- 4.2 Latent Classes for Bivariate Response of CD4/CD8 Ratio and CD4+T Within Groups of Adherence -- 5 Discussion and Conclusions -- References -- A Dynamic Model for Analyzing the Public Health Policy of the Mexican Government During the COVID-19 Pandemic -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 SIRD Model -- 3.2 Transmission Rate Model -- 3.3 Recovery Rate Estimation -- 3.4 Bayesian Inference -- 4 Results -- 5 Conclusions -- References -- Social Lag in the Municipalities of the State of Guerrero, México -- 1 Introduction -- 2 Methodology -- 2.1 CONEVAL -- 2.2 Clustering -- 3 Application -- 4 Results -- 4.1 CONEVAL -- 4.2 Cluster -- 4.3 Comparison -- 5 Conclusions -- References -- Challenges in Performing the Quick Counts of the National Electoral Institute in Mexico -- 1 Introduction -- 2 The Model -- 3 The Code -- 3.1 The Functional Code -- 3.2 First Idea: Use the Sample Design -- 3.3 Second Idea: Reach Out to the Community -- 4 Concluding Remarks -- References -- Sampling Design and Poststratification to Correct Lack of Information in Bayesian Quick Counts -- 1 Introduction -- 2 Model and Specifications -- 3 Sample Design -- 4 Incomplete Sample Estimation -- 4.1 General Strategy -- 4.2 Poststratification -- 4.3 Credibility Level Correction -- 5 Election Day -- 6 Conclusions.
References -- Maximum Likelihood Estimation for a Markov-Modulated Jump-Diffusion Model -- 1 Introduction -- 2 Markov-Modulated Jump-Diffusion Model -- 3 Methodology for the Maximum Likelihood Estimators -- 3.1 Identify the Jumps of the MJP -- 3.2 Estimate the Distribution of the Jumps -- 3.3 Estimate the Coefficients of the GBM -- 3.4 MLE of Q -- 4 Simulation Study -- 5 Real Data -- 6 Conclusions -- References -- Estimating the Composition of the Chamber of Deputies in the Quick Count for the 2021 Federal Election in Mexico -- 1 Introduction -- 1.1 The Quick Count -- 2 The Chamber of Deputies -- 2.1 Conformation -- 3 Estimation -- 3.1 Sampling Design -- 3.2 Bootstrap Estimation -- 4 Incomplete Samples -- 4.1 Multiple Imputation -- 5 Election Day: June 6, 2021 -- 5.1 Election Day Strategy -- 5.2 Arrival of Information -- 5.3 Estimations -- 6 Discussion -- A Appendix: Transforming Votes into Seats in the Deputy Chamber -- A.1 Relative Majority -- A.2 Voting Types Considered in the Law -- A.3 Proportional Representation -- References -- Bayesian Analysis of Homicide Rates in Mexico from 2000 to 2012 -- 1 Introduction -- 2 Literature Review -- 3 Data -- 4 Model Specification -- 5 Analysis and Results -- 6 Concluding Remarks -- References -- Index.
Record Nr. UNINA-9910633937503321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Interdisciplinary statistics in Mexico : AME virtual meeting, September 10-11, 2020, and 34 FNE, Acatlán, Mexico, September 22-24, 2021 / / Isadora Antoniano-Villalobos [and three others] editors
Interdisciplinary statistics in Mexico : AME virtual meeting, September 10-11, 2020, and 34 FNE, Acatlán, Mexico, September 22-24, 2021 / / Isadora Antoniano-Villalobos [and three others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (234 pages)
Disciplina 519.5
Collana Springer proceedings in mathematics & statistics
Soggetto topico Mathematical statistics
Statistics
Estadística matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-031-12778-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- A Methodological Proposal to Model and Evaluate the Complexity of the Mexican Geo-Electoral System -- 1 Introduction -- 2 Methodological Framework -- 2.1 Conceptualization of Electoral Complexity -- 2.2 Statistical Indicators for Quantifying Electoral Complexity -- 2.3 Data Transformation, Electoral Complexity Indices, and Stratification -- 3 ECI Construction and Stratification -- 3.1 Exploratory Analysis and PCA Implementation -- 3.2 Clustering Analysis and Stratification with K-Means -- 4 Electoral Complexity Ranking and Stratification Results -- 5 Conclusions -- References -- A Spatial Analysis of Drug Dealing in Mexico City -- 1 Introduction -- 2 Background -- 2.1 Drug Dealing as Part of Organized Crime -- 2.2 Research Approaches -- 3 Methodology -- 4 Results -- 4.1 Univariate Global and Local Moran's I -- 4.2 Bivariate Global and Local Moran's I -- 5 Discussion and Conclusions -- References -- Bayesian Hierarchical Multinomial Modeling of the 2021 Mexican Election Outcomes with Censored Samples -- 1 Introduction -- 2 Background -- 2.1 Estimation Methods in the 2021 Quick Count -- 2.2 NBM Model Background -- 2.3 The Bias Problem -- 3 The NBM Model -- 3.1 Specification -- 3.2 Consistency of Our Modeling Assumptions -- 3.3 Fitting Procedure -- 3.4 Model Adequacy -- 4 The 2021 Mexican Elections -- 4.1 Data and Sample Design -- 4.2 Results -- 5 Conclusions and Future Work -- References -- Assessing Hospitalization for SARS-CoV-2 Confirmed Cases by a Cross-Entropy Weighted Ensemble Classifier -- 1 Introduction -- 2 Material -- 2.1 Dataset Description -- 2.2 Data Analysis -- 3 Cross-entropy Weighted Ensemble Classifier -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Emotion Analysis to Identify Risk of Committing Suicide Using Statistical Learning -- 1 Background -- 2 Materials and Methods.
2.1 Emotion Mining -- 2.2 Statistical Learning Methods -- 2.3 Training and Test Datasets -- 3 Results -- 3.1 Supervised Models -- 3.2 Unsupervised Model -- 3.3 Model Comparison -- 3.4 Testing the Models with a New Test Set: COVID-19 -- 4 Conclusions -- References -- Characterizing Groups Using Latent Class Mixed Models: Antiretroviral Treatment Adherence Analysis -- 1 Introduction -- 2 Materials -- 2.1 Population -- 2.2 Data Collection -- 2.3 ART Adherence Definition -- 3 Latent Class Mixture Models -- 4 Results -- 4.1 Latent Classes from ART Adherence -- 4.2 Latent Classes for Bivariate Response of CD4/CD8 Ratio and CD4+T Within Groups of Adherence -- 5 Discussion and Conclusions -- References -- A Dynamic Model for Analyzing the Public Health Policy of the Mexican Government During the COVID-19 Pandemic -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 SIRD Model -- 3.2 Transmission Rate Model -- 3.3 Recovery Rate Estimation -- 3.4 Bayesian Inference -- 4 Results -- 5 Conclusions -- References -- Social Lag in the Municipalities of the State of Guerrero, México -- 1 Introduction -- 2 Methodology -- 2.1 CONEVAL -- 2.2 Clustering -- 3 Application -- 4 Results -- 4.1 CONEVAL -- 4.2 Cluster -- 4.3 Comparison -- 5 Conclusions -- References -- Challenges in Performing the Quick Counts of the National Electoral Institute in Mexico -- 1 Introduction -- 2 The Model -- 3 The Code -- 3.1 The Functional Code -- 3.2 First Idea: Use the Sample Design -- 3.3 Second Idea: Reach Out to the Community -- 4 Concluding Remarks -- References -- Sampling Design and Poststratification to Correct Lack of Information in Bayesian Quick Counts -- 1 Introduction -- 2 Model and Specifications -- 3 Sample Design -- 4 Incomplete Sample Estimation -- 4.1 General Strategy -- 4.2 Poststratification -- 4.3 Credibility Level Correction -- 5 Election Day -- 6 Conclusions.
References -- Maximum Likelihood Estimation for a Markov-Modulated Jump-Diffusion Model -- 1 Introduction -- 2 Markov-Modulated Jump-Diffusion Model -- 3 Methodology for the Maximum Likelihood Estimators -- 3.1 Identify the Jumps of the MJP -- 3.2 Estimate the Distribution of the Jumps -- 3.3 Estimate the Coefficients of the GBM -- 3.4 MLE of Q -- 4 Simulation Study -- 5 Real Data -- 6 Conclusions -- References -- Estimating the Composition of the Chamber of Deputies in the Quick Count for the 2021 Federal Election in Mexico -- 1 Introduction -- 1.1 The Quick Count -- 2 The Chamber of Deputies -- 2.1 Conformation -- 3 Estimation -- 3.1 Sampling Design -- 3.2 Bootstrap Estimation -- 4 Incomplete Samples -- 4.1 Multiple Imputation -- 5 Election Day: June 6, 2021 -- 5.1 Election Day Strategy -- 5.2 Arrival of Information -- 5.3 Estimations -- 6 Discussion -- A Appendix: Transforming Votes into Seats in the Deputy Chamber -- A.1 Relative Majority -- A.2 Voting Types Considered in the Law -- A.3 Proportional Representation -- References -- Bayesian Analysis of Homicide Rates in Mexico from 2000 to 2012 -- 1 Introduction -- 2 Literature Review -- 3 Data -- 4 Model Specification -- 5 Analysis and Results -- 6 Concluding Remarks -- References -- Index.
Record Nr. UNISA-996499866703316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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An Introduction to Statistical Learning [[electronic resource] ] : with Applications in Python / / by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor
An Introduction to Statistical Learning [[electronic resource] ] : with Applications in Python / / by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor
Autore James Gareth
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (617 pages)
Disciplina 519.5
Altri autori (Persone) WittenDaniela
HastieTrevor
TibshiraniRobert
TaylorJonathan
Collana Springer Texts in Statistics
Soggetto topico Statistics
Mathematical statistics - Data processing
Statistical Theory and Methods
Statistics and Computing
Applied Statistics
Estadística matemàtica
Models matemàtics
Python (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 9783031387470
3-031-38747-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Statistical Learning -- Linear Regression -- Classification -- Resampling Methods -- Linear Model Selection and Regularization -- Moving Beyond Linearity -- Tree-Based Methods -- Support Vector Machines -- Deep Learning -- Survival Analysis and Censored data -- Unsupervised Learning -- Multiple Testing -- Index.
Record Nr. UNINA-9910734891903321
James Gareth  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
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