LITMIR.BIZ

Скачать книгу

Estimation for LME Model6.2.1 Maximum Likelihood Estimation Method6.2.2 Distribution-Free Estimation Methods6.3 Hypothesis Testing6.3.1 Testing for Fixed Effects6.3.2 Testing for Variance–Covariance ParametersBibliographic NotesExercises

      11 Part 2 Random Effects Approaches for Diagnosis and PrognosisChapter 7: Diagnosis of Variation Source Using PCA7.1 Linking Variation Sources to PCA7.2 Diagnosis of Single Variation Source7.3 Diagnosis of Multiple Variation Sources7.4 Data Driven Method for Diagnosing Variation SourcesBibliographic NotesExercisesChapter 8: Diagnosis of Variation Sources Through Random Effects Estimation8.1 Estimation of Variance Components8.2 Properties of Variation Source Estimators8.3 Performance Comparison of Variance Component EstimatorsBibliographic NotesExercisesChapter 9: Analysis of System Diagnosability9.1 Diagnosability of Linear Mixed Effects Model9.2 Minimal Diagnosable Class9.3 Measurement System Evaluation Based on System DiagnosabilityBibliographic NotesExercisesAppendixChapter 10: Prognosis Through Mixed Effects Models for Longitudinal Data10.1 Mixed Effects Model for Longitudinal Data10.2 Random Effects Estimation and Prediction for an Individual Unit10.3 Estimation of Time-to-Failure Distribution10.4 Mixed Effects Model with Mixture Prior Distribution10.4.1 Mixture Distribution10.4.2 Mixed Effects Model with Mixture Prior for Longitudinal Data10.5 Recursive Estimation of Random Effects Using Kalman Filter10.5.1 Introduction to the Kalman Filter10.5.2 Random Effects Estimation Using the Kalman FilterBiographical NotesExercisesAppendixChapter 11: Prognosis Using Gaussian Process Model11.1 Introduction to Gaussian Process Model11.2 GP Parameter Estimation and GP Based Prediction11.3 Pairwise Gaussian Process Model11.3.1 Introduction to Multi-output Gaussian Process11.3.2 Pairwise GP Modeling Through Convolution Process11.4 Multiple Output Gaussian Process for Multiple Signals11.4.1 Model Structure11.4.2 Model Parameter Estimation and Prediction11.4.3 Time-to-Failure Distribution Based on GP PredictionsBibliographical NotesExercisesChapter 12: Prognosis Through Mixed Effects Models for Time-to-Event Data12.1 Models for Time-to-Event Data Without Covariates12.1.1 Parametric Models for Time-to-Event Data12.1.2 Non-parametric Models for Time-to-Event Data12.2 Survival Regression Models12.2.1 Cox PH Model with Fixed Covariates12.2.2 Cox PH Model with Time Varying Covariates12.2.3 Assessing Goodness of Fit12.3 Joint Modeling of Time-to-Event Data and Longitudinal Data12.3.1 Structure of Joint Model and Parameter Estimation12.3.2 Online Event Prediction for a New Unit12.4 Cox PH Model with Frailty Term for Recurrent EventsBibliographical NotesExercisesAppendix

      12  Appendix: Basics of Vectors, Matrices, and Linear Vector Space

      13  References

      14  Index

      Guide

      1  Cover

      2  Title page

      3  Copyright

      4  Dedication

      5 Table of Contents

      6  Preface

      7  Acknowledgments

      8  Acronyms

      9  Table of Notation

      10  Begin Reading

      11  Appendix: Basics of Vectors, Matrices, and Linear Vector Space

      12  References

      13  Index

      14  End User License Agreement

      Pages

      1  i

      2  ii

      3  iii

      4  iv

      5  v

      6 vi

      7  vii

      8  viii

      9  ix

      10  x

      11  xi

      12  xii

      13  xiii

      14  xiv

      15  xv

      16  xvi

      17  xvii

      18  xviii

      19  1

      20  2

      21  3

      22  4

Скачать книгу