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Applied multivariate statistical analysis in medicine (Record no. 8367)

MARC details
000 -LEADER
fixed length control field	06530nam a22001937a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20250129165122.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	250129b \|\|\|\|\|\|\|\| \|\|\|\| 00\| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9780443235870
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	519.535
Item number	JIA
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Jiang, Jingmei
245 ## - TITLE STATEMENT
Title	Applied multivariate statistical analysis in medicine
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc.	Academic Press
Place of publication, distribution, etc.	Cambridge
Date of publication, distribution, etc.	2024
365 ## - TRADE PRICE
Price type code	USD
Price amount	165.00
500 ## - GENERAL NOTE
General note	Table of content:<br/>1. Overview of multivariate statistical analysis<br/><br/>1.1 Introduction<br/><br/>1.2 Application of multivariate statistical analysis<br/><br/>1.3 Structure of multivariate data<br/><br/>1.4 Descriptive statistics of multivariate data<br/><br/>1.5 Statistical distance<br/><br/>1.6 Statistical software<br/><br/>1.7 Problems<br/><br/><br/>2. Multivariate normal distribution<br/><br/>2.1 Introduction<br/><br/>2.2 Distributions of random vectors<br/><br/>2.3 Numerical characteristics of random vectors<br/><br/>2.4 Multivariate normal distribution<br/><br/>2.5 Parameter estimation of the multivariate normal distribution<br/><br/>2.6 Calculation of the reference region<br/><br/>2.7 Detecting outliers<br/><br/>2.8 Summary<br/><br/>2.9 Problems<br/><br/><br/>3. Hypothesis testing for the parameters of multivariate normal populations<br/><br/>3.1 Introduction<br/><br/>3.2 Distributions of several important statistics<br/><br/>3.3 Hypothesis testing<br/><br/>3.4 Multivariate analysis of variance<br/><br/>3.5 Testing for the homogeneity of covariance matrices<br/><br/>3.6 Data transformation<br/><br/>3.7 Summary<br/><br/>3.8 Problems<br/><br/><br/>4. Multivariate linear regression<br/><br/>4.1 Introduction<br/><br/>4.2 Classical multivariate linear regression model<br/><br/>4.3 Hypothesis tests for models and regression coefficients<br/><br/>4.4 Evaluation of model fit and variable selection<br/><br/>4.5 Diagnosis and treatment of multicollinearity<br/><br/>4.6 Other issues in multivariate linear regression<br/><br/>4.7 Summary<br/><br/>4.8 Problems<br/><br/><br/>5. Generalized linear models<br/><br/>5.1 Introduction<br/><br/>5.2 Overview of generalized linear models<br/><br/>5.3 Data representation of generalized linear models<br/><br/>5.4 Distribution of response variables<br/><br/>5.5 Exponential family and generalized linear models<br/><br/>5.6 Parameter estimation for generalized linear models<br/><br/>5.7 Hypothesis testing for generalized linear models<br/><br/>5.8 Goodness-of-fit test of generalized linear models<br/><br/>5.9 Application of generalized linear models<br/><br/>5.10 Summary<br/><br/>5.11 Problems<br/><br/><br/>6. Logistic regression<br/><br/>6.1 Introduction<br/><br/>6.2 Logit behind logistic regression models<br/><br/>6.3 Binary logistic regression<br/><br/>6.4 Logistic regression for matched case-control studies<br/><br/>6.5 Logistic regression for multinomial outcomes<br/><br/>6.6 Logistic regression for ordinal outcomes<br/><br/>6.7 Other issues for logistic regression<br/><br/>6.8 Summary<br/><br/>6.9 Problems<br/><br/><br/>7. Survival analysis<br/><br/>7.1 Introduction<br/><br/>7.2 Overview for survival analysis<br/><br/>7.3 Modeling the hazard function<br/><br/>7.4 Exponential model<br/><br/>7.5 Weibull model<br/><br/>7.6 Cox proportional hazard model<br/><br/>7.7 Extensions to the Cox proportional hazard model<br/><br/>7.8 Summary<br/><br/>7.9 Problems<br/><br/><br/>8. Principal component analysis<br/><br/>8.1 Introduction<br/><br/>8.2 Population principal components<br/><br/>8.3 Sample principal components<br/><br/>8.4 Steps of principal component analysis<br/><br/>8.5 Application of principal component analysis<br/><br/>8.6 Summary<br/><br/>8.7 Problems<br/><br/><br/>9. Factor analysis<br/><br/>9.1 Introduction<br/><br/>9.2 Exploratory factor analysis<br/><br/>9.3 Confirmatory factor analysis<br/><br/>9.4 Steps of factor analysis<br/><br/>9.5 Other issues in factor analysis<br/><br/>9.6 Summary<br/><br/>9.7 Problems<br/><br/><br/>10. Canonical correlation analysis<br/><br/>10.1 Introduction<br/><br/>10.2 Review of correlation<br/><br/>10.3 Population canonical correlations<br/><br/>10.4 Sample canonical correlations<br/><br/>10.5 Canonical redundancy analysis<br/><br/>10.6 Other issues in canonical correlation analysis<br/><br/>10.7 Summary<br/><br/>10.8 Problems<br/><br/><br/>11. Cluster analysis<br/><br/>11.1 Introduction<br/><br/>11.2 Measures of similarity<br/><br/>11.3 Definition and characteristics of clusters<br/><br/>11.4 Hierarchical clustering methods<br/><br/>11.5 Dynamic clustering method<br/><br/>11.6 Ordered object clustering<br/><br/>11.7 Other issues in cluster analysis<br/><br/>11.8 Summary<br/><br/>11.9 Problems<br/><br/><br/>12. Discriminant analysis<br/><br/>12.1 Introduction<br/><br/>12.2 Discrimination using the mahalanobis distance<br/><br/>12.3 Fisher discriminant<br/><br/>12.4 Bayes discriminant<br/><br/>12.5 Stepwise discriminant<br/><br/>12.6 Other issues for discriminant analysis<br/><br/>12.7 Summary<br/><br/>12.8 Problems<br/><br/><br/>13. matrix algebra<br/><br/>13.1 Introduction<br/><br/>13.2 Basic concept of a vector<br/><br/>13.3 Basic concept of a matrix<br/><br/>13.4 Determinant, inverse, and rank of a matrix<br/><br/>13.5 Eigenvalue, eigenvectors, and trace of a matrix<br/><br/>13.6 Quadratic forms, spectral decomposition, and positive definite matrix<br/><br/>13.7 Elimination transformation<br/><br/>13.8 Derivative of the matrix<br/><br/>13.9 Summary<br/><br/>13.10 Problems<br/><br/>[https://shop.elsevier.com/books/applied-multivariate-statistical-analysis-in-medicine/jiang/978-0-443-23587-0]
520 ## - SUMMARY, ETC.
Summary, etc.	Applied 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.<br/><br/>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.<br/><br/>(https://shop.elsevier.com/books/applied-multivariate-statistical-analysis-in-medicine/jiang/978-0-443-23587-0)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Medical statistics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element	Multivariate analysis
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	Book
Source of classification or shelving scheme	Dewey Decimal Classification

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