"Statistical Methodologies" ed. by Jan Peter Hessling

Conceptual simplified studies of the kind presented in this book could therefore provide valuable guidance when developing statistical methodologies, but also applying state of the art with greater confidence.

Statistical practices have recently been questioned by numerous independent authors, to the extent that a significant fraction of accepted research findings can be questioned. This suggests that statistical methodologies may have gone too far into an engineering practice, with minimal concern for their foundation, interpretation, assumptions, and limitations, which may be jeopardized in the current context. Disguised by overwhelming data sets, advanced processing, and stunning presentations, the basic approach is often intractable to anyone but the analyst. The hierarchical nature of statistical inference, exemplified by Bayesian aggregation of prior and derived knowledge, may also be challenging.


Contents
1.Introductory Chapter: Ramifications of Incomplete Knowledge
2.Surveying Sensitive Topics with Indirect Questioning
3.Development of Estimation Procedure of Population Mean in Two-Phase Stratified Sampling
4.Methods of Russian Patent Analysis
5.Density Estimation in Inventory Control Systems under a Discounted Optimality Criterion
6.A Comparative Study of Maximum Likelihood Estimation and Bayesian Estimation for Erlang Distribution and Its Applications
7.Asymptotic Normality of Hill’s Estimator under Weak Dependence
8.A Study on the Comparison of the Effectiveness of the Jackknife Method in the Biased Estimators