05814nam 2200685 450 991013235060332120170810190648.01-118-85396-21-118-85392-X1-118-85393-8(CKB)3710000000251854(EBL)1810511(SSID)ssj0001348340(PQKBManifestationID)11775629(PQKBTitleCode)TC0001348340(PQKBWorkID)11372227(PQKB)11618509(MiAaPQ)EBC1810511(DLC) 2014011507(CaSebORM)9781118853962(PPN)192687441(EXLCZ)99371000000025185420141014h20142014 uy 0engur|n|---|||||txtccrStatistical inference for models with multivariate t-distributed errors /A. K. Md. Ehsanes Saleh, M. Arashi, S. M. M. Tabatabaey1st editionHoboken, New Jersey :John Wiley & Sons,2014.©20141 online resource (275 p.)Description based upon print version of record.1-322-19613-3 1-118-85405-5 Includes bibliographical references and indexes.Cover; Title Page; Copyright Page; CONTENTS; List of Figures; List of Tables; Preface; Glossary; List of Symbols; 1 Introduction; 1.1 Objective of the Book; 1.2 Models under Consideration; 1.2.1 Location Model; 1.2.2 Simple Linear Model; 1.2.3 ANOVA Model; 1.2.4 Parallelism Model; 1.2.5 Multiple Regression Model; 1.2.6 Ridge Regression; 1.2.7 Multivariate Model; 1.2.8 Simple Multivariate Linear Model; 1.3 Organization of the Book; 1.4 Problems; 2 Preliminaries; 2.1 Normal Distribution; 2.2 Chi-Square Distribution; 2.3 Student''s t-Distribution; 2.4 F-Distribution2.5 Multivariate Normal Distribution2.6 Multivariate t-Distribution; 2.6.1 Expected Values of Functions of M_t^(p)(η , σ^2V_p, γo) - Variables; 2.6.2 Sampling Distribution of Quadratic Forms; 2.6.3 Distribution of Linear Functions of t-Variables; 2.7 Problems; 3 Location Model; 3.1 Model Specification; 3.2 Unbiased Estimates of θ and σ^2 and Test of Hypothesis; 3.3 Estimators; 3.4 Bias and MSE Expressions of the Location Estimators; 3.4.1 Analysis of the Estimators of Location Parameter; 3.5 Various Estimates of Variance; 3.5.1 Bias and MSE Expressions of the Variance Estimators3.5.2 Analysis of the Estimators of the Variance Parameter3.6 Problems; 4 Simple Regression Model; 4.1 Introduction; 4.2 Estimation and Testing of η; 4.2.1 Estimation of η; 4.2.2 Test of Intercept Parameter; 4.2.3 Estimators of β and θ; 4.3 Properties of Intercept Parameter; 4.3.1 Bias Expressions of the Estimators; 4.3.2 MSE Expressions of the Estimators; 4.4 Comparison; 4.4.1 Optimum Level of Significance of θ_n^PT; 4.5 Numerical Illustration; 4.6 Problems; 5 ANOVA; 5.1 Model Specification; 5.2 Proposed Estimators and Testing; 5.3 Bias, MSE, and Risk Expressions; 5.4 Risk Analysis5.4.1 Comparison of θ_n and θ_n5.4.2 Comparison of θ_n_PT and θ_n(θ_n); 5.4.3 Comparison of θ_n^S, θ_n , θn, and θ_n^PT; 5.4.4 Comparison of θ_n^S and θ_n^S+; 5.5 Problems; 6 Parallelism Model; 6.1 Model Specification; 6.2 Estimation of the Parameters and Test of Parallelism; 6.2.1 Test of Parallelism; 6.3 Bias, MSE, and Risk Expressions; 6.3.1 Expressions of Bias, MSE Matrix, and Risks of β_n, Θ_n, β_n, and Θ_n; 6.3.2 Expressions of Bias, MSE Matrix, and Risks of the PTEs of β and Θ; 6.3.3 Expressions of Bias, MSE Matrix, and Risks of the SSEs of β and Θ6.3.4 Expressions of Bias, MSE Matrix, and Risks of the PRSEs of β and Θ6.4 Risk Analysis; 6.5 Problems; 7 Multiple Regression Model; 7.1 Model Specification; 7.2 Shrinkage Estimators and Testing; 7.3 Bias and Risk Expressions; 7.3.1 Balanced Loss Function; 7.3.2 Properties; 7.4 Comparison; 7.5 Problems; 8 Ridge Regression; 8.1 Model Specification; 8.2 Proposed Estimators; 8.3 Bias, MSE, and Risk Expressions; 8.3.1 Biases of the Estimators; 8.3.2 MSE Matrices and Risks of the Estimators; 8.4 Performance of the Estimators; 8.4.1 Comparison between β_n(k), β_n^S(k), and β_n^S+(k)8.4.2 Comparison between β_n (k) and β_n^PT (k)"This book summarizes the results of various models under normal theory with a brief review of the literature. Statistical Inference for Models with Multivariate t-Distributed Errors: Includes a wide array of applications for the analysis of multivariate observations Emphasizes the development of linear statistical models with applications to engineering, the physical sciences, and mathematics Contains an up-to-date bibliography featuring the latest trends and advances in the field to provide a collective source for research on the topic Addresses linear regression models with non-normal errors with practical real-world examples Uniquely addresses regression models in Student's t-distributed errors and t-models Supplemented with an Instructor's Solutions Manual, which is available via written request by the Publisher "--Provided by publisher.Regression analysisMultivariate analysisRegression analysis.Multivariate analysis.519.536MAT029030MAT029010MAT029020bisacshSaleh A. K. Md. Ehsanes150888Arashi M.Tabatabaey S. M. M.MiAaPQMiAaPQMiAaPQBOOK9910132350603321Statistical inference for models with multivariate t-distributed errors1984568UNINA03044oam 22004934a 450 991028644090332120230621141053.0607-628-651-2968-12-0945-1(CKB)4100000006673407(OCoLC)1055624216(MdBmJHUP)muse85026(WaSeSS)IndRDA00124483(oapen)https://directory.doabooks.org/handle/20.500.12854/89705(oapen)doab89705(EXLCZ)99410000000667340720030801d1999 uy 0spaur|||||||nn|ntxtrdacontentcrdamediacrrdacarrierFederalización e innovación educativa en MéxicoMaría del Carmen Pardo (coordinadora)Primera edición.El Colegio de México1999Mexico :El Colegio de Mexico, Centro de Estudios Internacionales,1999.©1999.1 online resource (578 p.)En este libro se recogen estudios que permiten entender el proceso de descentralización de los servicios de educación básica transferidos por el gobierno federal a los gobiernos estatales a partir del Acuerdo Nacional para la Modernización de la Educación Básica de mayo de 1992. Estos estudios están divididos en dos grandes apartados: los referidos a aspectos generales del proceso de la descentralización educativa en los que resaltan tanto los antencedentes históricos de ese cambio, como el papel de actores fundamentales en dicho proceso, la Secretaría de Educación Pública (SEP) y el Sindicato Nacional de Trabajadores de la Educación (SNTE), así como los llamados estudios de caso que incorporan un análisis detallado de las formas tanto administrativas como pedagógicas ensayadas en cinco estados de la República para recibir y gestionar los servicios educativos transferidos. Aguascalientes, Guanajuato, Chihuahua, Nuevo León y Oaxaca fueron los estados seleccionados para observar la transferencia de los servicios de educación básica, subrayando de manera destacada la oportunidad que tendrían para generar innovaciones en el campo educativo, a partir del momento en que se convirtieran en responsabilidad de los respectivos gobiernos estatales. En el análisis de los cinco estados se incorporaron elementos que permitieron hacer un ejercicio de comparación y llegar a conclusiones preliminares, puesto que se trata de un proceso en gestación.Política educativaMexicoEducacióMexicoEducationPolítica educativaEducació379.72Pardo María del CarmenauthPardo Maria del Carmen1023130El Colegio de Mexico.Centro de Estudios Internacionales.MdBmJHUPMdBmJHUPBOOK9910286440903321Federalización e innovación educativa en México2430597UNINA02344oam 2200493I 450 991016285740332120251107230832.01-4822-5862-51-315-37226-61-4822-5860-910.1201/9781315372266(CKB)3710000001021899(MiAaPQ)EBC4778632(OCoLC)967412430(EXLCZ)99371000000102189920180706h20172017 uy 0engurcnu||||||||rdacontentrdamediardacarrierStatistical modeling and machine learning for molecular biology /Alan Moses, University of Toronto, Canada1st ed.Boca Raton :CRC Press,[2017]©20171 online resourceChapman & Hall/CRC mathematical and computational biology series1-138-40721-6 1-4822-5859-5 section 1. Overview -- section 2. Clustering -- section 3. Regression -- section 4. Classification.Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.Chapman and Hall/CRC mathematical & computational biology series.Molecular biologyStatistical methodsMolecular biologyData processingMolecular biologyStatistical methods.Molecular biologyData processing.572.8Moses Alan M.1855674FlBoTFGFlBoTFGBOOK9910162857403321Statistical modeling and machine learning for molecular biology4453978UNINA