01519nam 2200349Ia 450 99639725810331620210104172046.0(CKB)4940000000063224(EEBO)2240867905(OCoLC)ocm62369664e(OCoLC)62369664(EXLCZ)99494000000006322420051129d1687 uy 0engurbn||||a|bb|Grammatica Anglo-Romana, or, A syncritical grammar[electronic resource] teaching English youth the Latin tongue by few and easie rules comparing English with Latin : with a comment for the use of riper years, containing the elegancies and explaining the difficult phrases and idioms which are particular to the Latin, fitted to the sense of the learned Oxford commentators upon Lilly's grammar /by Samuel ShawLondon Printed for Robert Clavel at the Sign of the Peacock in St. Paul's Church Yard1687[14], 223, [1] pPublisher's advertisements at end: p. [1].Includes errata : p. [14]Imperfect: pages tightly bound.Reproduction of original in: British Libraryeebo-0018Latin languageGrammarEarly works to 1800Latin languageGrammarShaw Samuel1635-1696.821526UMIUMIBOOK996397258103316Grammatica Anglo-Romana, or, A syncritical grammar2411424UNISA07401nam 2200553 450 991055519820332120220328123742.01-5231-4353-31-119-66630-91-119-66627-91-119-66629-5(CKB)4100000011979753(MiAaPQ)EBC6675141(Au-PeEL)EBL6675141(OCoLC)1260343508(EXLCZ)99410000001197975320220328d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierIndustrial data analytics for diagnosis and prognosis a random effects modelling approach /Shiyu Zhou, Yong ChenHoboken, New Jersey :John Wiley & Sons, Inc.,[2021]©20211 online resource (353 pages)1-119-66628-7 Includes bibliographical references and index.Intro -- Industrial Data Analytics for Diagnosis and Prognosis -- Contents -- Preface -- Acknowledgments -- Acronyms -- Table of Notation -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Scope and Organization of the Book -- 1.3 How to Use This Book -- Bibliographic Note -- Part 1 Statistical Methods and Foundation for Industrial Data Analytics -- 2 Introduction to Data Visualization and Characterization -- 2.1 Data Visualization -- 2.1.1 Distribution Plots for a Single Variable -- 2.1.2 Plots for Relationship Between Two Variables -- 2.1.3 Plots for More than Two Variables -- 2.2 Summary Statistics -- 2.2.1 Sample Mean, Variance, and Covariance -- 2.2.2 Sample Mean Vector and Sample Covariance Matrix -- 2.2.3 Linear Combination of Variables -- Bibliographic Notes -- Exercises -- 3 Random Vectors and the Multivariate Normal Distribution -- 3.1 Random Vectors -- 3.2 Density Function and Properties of Multivariate Normal Distribution -- 3.3 Maximum Likelihood Estimation for Multivariate Normal Distribution -- 3.4 Hypothesis Testing on Mean Vectors -- 3.5 Bayesian Inference for Normal Distribution -- Bibliographic Notes -- Exercises -- 4 Explaining Covariance Structure: Principal Components -- 4.1 Introduction to Principal Component Analysis -- 4.1.1 Principal Components for More Than Two Variables -- 4.1.2 PCA with Data Normalization -- 4.1.3 Visualization of Principal Components -- 4.1.4 Number of Principal Components to Retain -- 4.2 Mathematical Formulation of Principal Components -- 4.2.1 Proportion of Variance Explained -- 4.2.2 Principal Components Obtained from the Correlation Matrix -- 4.3 Geometric Interpretation of Principal Components -- 4.3.1 Interpretation Based on Rotation -- 4.3.2 Interpretation Based on Low-Dimensional Approximation -- Bibliographic Notes -- Exercises.5 Linear Model for Numerical and Categorical Response Variables -- 5.1 Numerical Response - Linear Regression Models -- 5.1.1 General Formulation of Linear Regression Model -- 5.1.2 Significance and Interpretation of Regression Coefficients -- 5.1.3 Other Types of Predictors in Linear Models -- 5.2 Estimation and Inferences of Model Parameters for Linear Regression -- 5.2.1 Least Squares Estimation -- 5.2.2 Maximum Likelihood Estimation -- 5.2.3 Variable Selection in Linear Regression -- 5.2.4 Hypothesis Testing -- 5.3 Categorical Response - Logistic Regression Model -- 5.3.1 General Formulation of Logistic Regression Model -- 5.3.2 Significance and Interpretation of Model Coefficients -- 5.3.3 Maximum Likelihood Estimation for Logistic Regression -- Bibliographic Notes -- Exercises -- 6 Linear Mixed Effects Model -- 6.1 Model Structure -- 6.2 Parameter Estimation for LME Model -- 6.2.1 Maximum Likelihood Estimation Method -- 6.2.2 Distribution-Free Estimation Methods -- 6.3 Hypothesis Testing -- 6.3.1 Testing for Fixed Effects -- 6.3.2 Testing for Variance-Covariance Parameters -- Bibliographic Notes -- Exercises -- Part 2 Random Effects Approaches for Diagnosis and Prognosis -- 7 Diagnosis of Variation Source Using PCA -- 7.1 Linking Variation Sources to PCA -- 7.2 Diagnosis of Single Variation Source -- 7.3 Diagnosis of Multiple Variation Sources -- 7.4 Data Driven Method for Diagnosing Variation Sources -- Bibliographic Notes -- Exercises -- 8 Diagnosis of Variation Sources Through Random Effects Estimation -- 8.1 Estimation of Variance Components -- 8.2 Properties of Variation Source Estimators -- 8.3 Performance Comparison of Variance Component Estimators -- Bibliographic Notes -- Exercises -- 9 Analysis of System Diagnosability -- 9.1 Diagnosability of Linear Mixed Effects Model -- 9.2 Minimal Diagnosable Class.9.3 Measurement System Evaluation Based on System Diagnosability -- Bibliographic Notes -- Exercises -- Appendix -- 10 Prognosis Through Mixed Effects Models for Longitudinal Data -- 10.1 Mixed Effects Model for Longitudinal Data -- 10.2 Random Effects Estimation and Prediction for an Individual Unit -- 10.3 Estimation of Time-to-Failure Distribution -- 10.4 Mixed Effects Model with Mixture Prior Distribution -- 10.4.1 Mixture Distribution -- 10.4.2 Mixed Effects Model with Mixture Prior for Longitudinal Data -- 10.5 Recursive Estimation of Random Effects Using Kalman Filter -- 10.5.1 Introduction to the Kalman Filter -- 10.5.2 Random Effects Estimation Using the Kalman Filter -- Biographical Notes -- Exercises -- Appendix -- 11 Prognosis Using Gaussian Process Model -- 11.1 Introduction to Gaussian Process Model -- 11.2 GP Parameter Estimation and GP Based Prediction -- 11.3 Pairwise Gaussian Process Model -- 11.3.1 Introduction to Multi-output Gaussian Process -- 11.3.2 Pairwise GP Modeling Through Convolution Process -- 11.4 Multiple Output Gaussian Process for Multiple Signals -- 11.4.1 Model Structure -- 11.4.2 Model Parameter Estimation and Prediction -- 11.4.3 Time-to-Failure Distribution Based on GP Predictions -- Bibliographical Notes -- Exercises -- 12 Prognosis Through Mixed Effects Models for Time-to-Event Data -- 12.1 Models for Time-to-Event Data Without Covariates -- 12.1.1 Parametric Models for Time-to-Event Data -- 12.1.2 Non-parametric Models for Time-to-Event Data -- 12.2 Survival Regression Models -- 12.2.1 Cox PH Model with Fixed Covariates -- 12.2.2 Cox PH Model with Time Varying Covariates -- 12.2.3 Assessing Goodness of Fit -- 12.3 Joint Modeling of Time-to-Event Data and Longitudinal Data -- 12.3.1 Structure of Joint Model and Parameter Estimation -- 12.3.2 Online Event Prediction for a New Unit.12.4 Cox PH Model with Frailty Term for Recurrent Events -- Bibliographical Notes -- Exercises -- Appendix -- Appendix: Basics of Vectors, Matrices, and Linear Vector Space -- References -- Index.Random data (Statistics)Industrial managementMathematicsIndustrial engineeringStatistical methodsElectronic books.Random data (Statistics)Industrial managementMathematics.Industrial engineeringStatistical methods.658.00727Zhou Shiyu1970-1217547Chen YongMiAaPQMiAaPQMiAaPQBOOK9910555198203321Industrial data analytics for diagnosis and prognosis2815785UNINA