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020			_a9780443235870
082			_a519.535 _bJIA
100			_aJiang, Jingmei _919481
245			_aApplied multivariate statistical analysis in medicine
260			_bAcademic Press _aCambridge _c2024
365			_aUSD _b165.00
500			_aTable of content: 1. Overview of multivariate statistical analysis 1.1 Introduction 1.2 Application of multivariate statistical analysis 1.3 Structure of multivariate data 1.4 Descriptive statistics of multivariate data 1.5 Statistical distance 1.6 Statistical software 1.7 Problems 2. Multivariate normal distribution 2.1 Introduction 2.2 Distributions of random vectors 2.3 Numerical characteristics of random vectors 2.4 Multivariate normal distribution 2.5 Parameter estimation of the multivariate normal distribution 2.6 Calculation of the reference region 2.7 Detecting outliers 2.8 Summary 2.9 Problems 3. Hypothesis testing for the parameters of multivariate normal populations 3.1 Introduction 3.2 Distributions of several important statistics 3.3 Hypothesis testing 3.4 Multivariate analysis of variance 3.5 Testing for the homogeneity of covariance matrices 3.6 Data transformation 3.7 Summary 3.8 Problems 4. Multivariate linear regression 4.1 Introduction 4.2 Classical multivariate linear regression model 4.3 Hypothesis tests for models and regression coefficients 4.4 Evaluation of model fit and variable selection 4.5 Diagnosis and treatment of multicollinearity 4.6 Other issues in multivariate linear regression 4.7 Summary 4.8 Problems 5. Generalized linear models 5.1 Introduction 5.2 Overview of generalized linear models 5.3 Data representation of generalized linear models 5.4 Distribution of response variables 5.5 Exponential family and generalized linear models 5.6 Parameter estimation for generalized linear models 5.7 Hypothesis testing for generalized linear models 5.8 Goodness-of-fit test of generalized linear models 5.9 Application of generalized linear models 5.10 Summary 5.11 Problems 6. Logistic regression 6.1 Introduction 6.2 Logit behind logistic regression models 6.3 Binary logistic regression 6.4 Logistic regression for matched case-control studies 6.5 Logistic regression for multinomial outcomes 6.6 Logistic regression for ordinal outcomes 6.7 Other issues for logistic regression 6.8 Summary 6.9 Problems 7. Survival analysis 7.1 Introduction 7.2 Overview for survival analysis 7.3 Modeling the hazard function 7.4 Exponential model 7.5 Weibull model 7.6 Cox proportional hazard model 7.7 Extensions to the Cox proportional hazard model 7.8 Summary 7.9 Problems 8. Principal component analysis 8.1 Introduction 8.2 Population principal components 8.3 Sample principal components 8.4 Steps of principal component analysis 8.5 Application of principal component analysis 8.6 Summary 8.7 Problems 9. Factor analysis 9.1 Introduction 9.2 Exploratory factor analysis 9.3 Confirmatory factor analysis 9.4 Steps of factor analysis 9.5 Other issues in factor analysis 9.6 Summary 9.7 Problems 10. Canonical correlation analysis 10.1 Introduction 10.2 Review of correlation 10.3 Population canonical correlations 10.4 Sample canonical correlations 10.5 Canonical redundancy analysis 10.6 Other issues in canonical correlation analysis 10.7 Summary 10.8 Problems 11. Cluster analysis 11.1 Introduction 11.2 Measures of similarity 11.3 Definition and characteristics of clusters 11.4 Hierarchical clustering methods 11.5 Dynamic clustering method 11.6 Ordered object clustering 11.7 Other issues in cluster analysis 11.8 Summary 11.9 Problems 12. Discriminant analysis 12.1 Introduction 12.2 Discrimination using the mahalanobis distance 12.3 Fisher discriminant 12.4 Bayes discriminant 12.5 Stepwise discriminant 12.6 Other issues for discriminant analysis 12.7 Summary 12.8 Problems 13. matrix algebra 13.1 Introduction 13.2 Basic concept of a vector 13.3 Basic concept of a matrix 13.4 Determinant, inverse, and rank of a matrix 13.5 Eigenvalue, eigenvectors, and trace of a matrix 13.6 Quadratic forms, spectral decomposition, and positive definite matrix 13.7 Elimination transformation 13.8 Derivative of the matrix 13.9 Summary 13.10 Problems [https://shop.elsevier.com/books/applied-multivariate-statistical-analysis-in-medicine/jiang/978-0-443-23587-0]
520			_aApplied Multivariate Statistical Analysis in Medicine provides a multivariate conceptual framework that allows readers to understand the interconnectivity and interrelations among variables, which maintains the intrinsic precision of statistical theories. With a strong focus on the fundamental concepts of multivariate statistical analysis, the book also gives insight into the applications of multivariate distribution in biomedical fields. In 14 chapters, Applied Multivariate Statistical Analysis in Medicine covers the main topics of multivariate analysis methods widely used in health science research. The content is organized progressively from fundamental concepts to sophisticated methods. It begins with basic descriptive statistics in multivariate analysis and follows with parameter estimation, in addition to the hypothesis testing of a multivariate normal distribution, which has heavy applications in biomedical fields where the relationships among approximately normal variables are of great interest. Keeping mathematics to a minimum, considerable emphasis is placed on explanations and real-world applications of core principles to maintain a good balance between introducing theory and cultivating problem-solving skills. This book is a very valuable reference text for clinicians, medical researchers, and other researchers across medical and biomedical disciplines, all of whom confront increasingly complex statistical methods during the analysis and presentation of their results. (https://shop.elsevier.com/books/applied-multivariate-statistical-analysis-in-medicine/jiang/978-0-443-23587-0)
650			_aMedical statistics
650			_aMultivariate analysis _9877
942			_cBK _2ddc
999			_c8367 _d8367