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書(shū)目名稱(chēng)Growth Curve Models and Statistical Diagnostics影響因子(影響力)




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男生戴手銬

Logic Circuit Design with DGMOS Devices, of the regression coefficient is measured. For illustration, the practical examples studied in Chapter 2 are reanalyzed using the techniques of discordant outlier detection and influential observation identification.

Ornithologist

John A. Fuerst,Evgeny Sagulenkoalysis addressed in Chapter 5, a covariance-weighted perturbation scheme is employed to demonstrate the use of this procedure. To illustrate, the practical data sets discussed in previous chapters are reanalyzed using Bayesian local influence procedures. This analysis reveals that the Bayesian local

Gobble

Introduction,odel selection criteria with respect to covariance structures. In Section 1.4, the main approaches and results in this book on statistical diagnostics for growth curve models are outlined in a summarized form. Some preparatory materials related to matrix derivatives and matrix-variate distributions

ALT

Fabric

Fabric

雕鏤

Generalized Least Square Estimation,d through a variety of practical examples in biology, agriculture, and medical research. Some basic statistical inference of the GCM, such as generalized least square estimate (GLSEs) and the admissibility of estimates on linear combinations of regression coefficients, are discussed in detail. It is

Myocarditis

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Discordant Outlier And Influential Observation,ce approach. Under Rao’s simple covariance structure (SCS) discussed in Section 3.2 of Chapter 3 and unstructured covariance (UC), two of the most commonly encountered covariance structures for growth analysis, the multiple individual deletion model (MIDM) and the mean shift regression model (MSRM)