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Complex data statistical methods: R-based applications(Chinese Edition)
Editore:
Renmin University of China Press, 2000
ISBN 10: 7300163998
ISBN 13: 9787300163994
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paperback. Condizione: Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned. Codice articolo rev3504309281
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Complex data statistical methods: R-based applications(Chinese Edition)
Editore:
Renmin University of China Press, 2000
ISBN 10: 7300163998
ISBN 13: 9787300163994
Antico o usato
Paperback
Da: HPB-Red, Dallas, TX, U.S.A.
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Paperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! Codice articolo S_228111958
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Complex statistics - based on the application of R (universities graduate with book)(Chinese Edition)
ISBN 10: 7300163998
ISBN 13: 9787300163994
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Da: liu xing, Nanjing, JS, Cina
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paperback. Condizione: New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback. Pub Date: Unknown in Publisher: basic information of the Chinese People's University Press List Price: 33.00 yuan Author: Publisher: China People's University Press ISBN: 9.787.300.163.994 Yema: Revision: Binding: Folio: Published :2012-10-1 Printing time: the number of words: Product ID: 22.891.530 Description complex statistical method - based on the application of r free daily software analysis over 30 true data downloaded from foreign sites. including the cross-sectional data. longitudinal data and time series data. almost all classic methods and machine learning methods introduced by these data. The complicated statistical methods - based on the application of r characteristics: (1) data-oriented; (2) introduced the latest method (with the traditional methods of review); (3) r software entry and all examples of day URL of the code and data; (4) independent of each chapter. Complex statistical method - based on the application of r Audience including statistics. applied statistics. economics. mathematics. applied mathematics. actuarial. environmental. econometrics. biomedical undergraduate. master's and doctoral teachers and practitioners of various fields. The author Wu Xizhi. Peking University. Department of Mathematics and Mechanics undergraduate. Dr. Statistics at the University of North Carolina. Statistics. Renmin University professor and doctoral tutor. At the University of California. University of North Carolina. Nankai University. Renmin University of China. Peking University and other famous universities coached. Table of Contents Chapter 1 Introduction 1.1 as scientific statistics 1.2 data analysis of the practice 1.3 data. and may be used in the model 1.3.1 cross-sectional data: the dependent variable is the number of variables of the real axis 1.3.2 cross-sectional data: the dependent variable classification (qualitative) variables or frequency relations between 1.3.3 of longitudinal data. multi-level data. panel data. repeated observations 1.3.4 multivariate data variables: multivariate analysis 1.3.5 path model / structural equation model 1.3.6 multivariate time series data 1.4 r Software Starter 1.4.1 Introduction 1.4.2 hands-Chapter 2 cross-sectional data: the dependent variable as a simple regression of the real axis the number of variables 2.1 Review 2.2 simple linear model is not easy to deal with cross-sectional data 2.2.1 exponential transformation of standard linear regression a 2.2.2 survival analysis data of the cox regression model 2.2.3 Data multicollinearity situation: ridge regression. lasso regression. adaptive lasso regression. partial least squares regression 2.2.4 can not do any assumptions about the data: machine learning the regression method 2.2.5 decision tree regression (regression trees) 2.2.6boosting regression 2.2.7bagging regression 2.2.8 random forest regression 2.2.9 artificial neural network regression the 2.2.10 support vector machine regression 2.2.11 the stability of the several regression Wuzhe 2.2.12 method of cross-validation results and overfitting Chapter 3 cross-sectional data: for categorical variables and the dependent variable is the dependent variable frequency (count) variable 3.1 Classic logistic regression. probit regression a review 3.1.1logistic regression and probit regression 3.1.2 classic discriminant analysis 3.2 dependent variable as a categorical variable. since the variable containing categorical variables: machine learning classification methods 3.2.1 decision tree classifier (classification tree discriminant analysis and applies only to the number of independent variables ) random forest classification 3.2.2adaboost classification 3.2.3bagging Classification 3.2.4 3.2.5 3.2.6 Nearest Neighbor Classification 3.2.7 Classification% off cross-validation results 3.3 dependent variable frequency (count). support vector machine classification 3. Codice articolo FW033911
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