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

• Record Nr.: UNINA9910633937503321
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2022]; ©2022
• ISBN: 3-031-12778-1
• Descrizione fisica: 1 online resource (234 pages)
• Collana: Springer proceedings in mathematics & statistics ; ; Volume 397
• Disciplina: 519.5
• Soggetti: Mathematical statistics; Statistics; Estadística matemàtica; Congressos; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• 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.