new statistical books

الاحصائي كتب:

Bayesian Statistical Inference (Quantitative Applications in the Social Sciences)
By Gudmund R. Iversen


Publisher: Sage Publications, Inc
Number Of Pages: 80
Publication Date: 1984-11-01
ISBN-10 / ASIN: 0803923287
ISBN-13 / EAN: 9780803923287
Binding: Paperback


Product Description:

The theoretical importance of Bayesian inference, if not its practical validity, are generally acknowledged; but a reason for the lag in applications is that empirical researchers have lacked a grounding in the methodology. Iversen's volume provides this introduction.


After outlining the limitations of classical statistical inference, the author proceeds through a simple example to explain Bayes' theorem and how it may overcome these limitations. Typical Bayesian applications are shown, together with the strengths and weaknesses of the Bayesian approach.


An understanding of Bayesian inference places the hypothesis testing and confidence intervals used by classical statistical inference in clearer perspective. This monograph thus serves as a companion volume for Henkel's Tests of Significance (QASS vol 4).

الاحصائي كتب:

Tree Models of Similarity and Association (Quantitative Applications in the Social Sciences)
By James E. Corter


Publisher: Sage Publications, Inc
Number Of Pages: 72
Publication Date: 1996-04-02
ISBN-10 / ASIN: 0803957076
ISBN-13 / EAN: 9780803957077
Binding: Paperback


Product Description:

Clustering and tree models are widely used in the social and biological sciences to analyze similarity relations. Tree Models of Similarity and Association describes how matrices of similarities or associations among entities can be modeled using trees, and to explain some of the issues that arise in performing such analyses and correctly interpreting the results. James E. Corter clearly distinguishes ultrametric trees (fit by the techniques widely known as "hierarchical clustering") from additive trees and discusses how specific aspects of each type of tree can be interpreted through the use of applications as examples. He concludes with a discussion of when tree models might be preferable to spatial geometric models, such as those fit by multidimensional scaling (MDS) or principal components analysis (PCA).