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Über dieses Buch

This book aims to put strong reasonable mathematical senses in notions of objectivity and subjectivity for consistent estimations in a Polish group by using the concept of Haar null sets in the corresponding group. This new approach – naturally dividing the class of all consistent estimates of an unknown parameter in a Polish group into disjoint classes of subjective and objective estimates – helps the reader to clarify some conjectures arising in the criticism of null hypothesis significance testing. The book also acquaints readers with the theory of infinite-dimensional Monte Carlo integration recently developed for estimation of the value of infinite-dimensional Riemann integrals over infinite-dimensional rectangles. The book is addressed both to graduate students and to researchers active in the fields of analysis, measure theory, and mathematical statistics.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Calculation of Improper Integrals by Using Uniformly Distributed Sequences

A certain modified version of Kolmogorov’s strong law of large numbersStrong law of large numbers is used for an extension of the result of C. Baxa and J. Schoi $$\beta $$ engeier (2002) to a maximal set of uniformly distributed sequences in (0, 1) that strictly contains the set of all sequences having the form $$(\{\alpha n\})_{n \in \mathbf{N}}$$ for some irrational number $$\alpha $$ and having the full $$\ell _1^{\infty }$$ -measure, where $$\ell _1^{\infty }$$ denotes the infinite power of the linear Lebesgue measure $$\ell _1$$ in (0, 1).

Gogi Pantsulaia

Chapter 2. Infinite-Dimensional Monte Carlo Integration

By using the main properties of uniformly distributed sequences of increasing finite sets in infinite-dimensional rectangles in $$R^{\infty }$$ R∞ described in [P2], an infinite-dimensional Monte Carlo integrationMonte-Carlo integration is elaborated and the validity of some new strong law type theorems are obtained in this chapter.

Gogi Pantsulaia

Chapter 3. Structure of All Real-Valued Sequences Uniformly Distributed in from the Point of View of Shyness

In [P5], it was shown that $$\mu $$ μ almost every element of $$\mathbf {R}^{\infty }$$ R∞ is uniformly distributed in $$[-\frac{{1}}{{2}}, \frac{{1}}{{2}}]$$ [-12,12], where $$\mu $$ μ denotes the Moore–Yamasaki–KharazishviliMoore–Yamasaki–Kharazishvili type measure measure in $$\mathbf {R}^{\infty }$$ R∞ for which $$\mu ([-\frac{1}{2},\frac{1}{2}]^{\infty }) = 1$$ μ([-12,12]∞)=1. In the present chapter the same set is studied from the point of view of shyness and it is demonstrated that it is shy in $$\mathbf {R}^{\infty }$$ R∞. In the Solovay modelSolovay Model (SM), the structure of the set of all sequences uniformly distributed modulo 1Uniformly distributed modulo 1 in $$[-\frac{{1}}{{2}}, \frac{{1}}{{2}}]$$ [-12,12] is studied from the point of view of shyness and it is shown that it is the prevalent setPrevalent set in $$\mathbf {R}^{\infty }$$ R∞.

Gogi Pantsulaia

Chapter 4. On Moore–Yamasaki–Kharazishvili Type Measures and the Infinite Powers of Borel Diffused Probability Measures on R

This chapter contains a brief description of Yamasaki’s remarkable investigation (1980) of the relationship between Moore–Yamasaki–Kharazishvili type measuresType measure and infinite powers of Borel diffused probability measures on $$\mathbf{R}$$ R. More precisely, there is given Yamasaki’s proof that no infinite power of the Borel probability measure with a strictly positive density function on R has an equivalent Moore–Yamasaki–Kharazishvili type measureType measure. A certain modification of Yamasaki’s example is used for the construction of such a Moore–Yamasaki–Kharazishvili type measureType measure that is equivalent to the product of a certain infinite family of Borel probability measures with a strictly positive density function on R. By virtue the properties of real-valued sequences equidistributed on the real axis, it is demonstrated that an arbitrary family of infinite powers of Borel diffused probability measuresDiffused probability measure with strictly positive density functions on R is strongly separated and, accordingly, has an infinite-sample well-founded estimator of the unknown distribution function. This extends the main result established in the paper [ZPS].

Gogi Pantsulaia

Chapter 5. Objective and Strong Objective Consistent Estimates of Unknown Parameters for Statistical Structures in a Polish Group Admitting an Invariant Metric

By using the notion of a Haar ambivalentHaar ambivalent set introduced by Balka, Buczolich, and Elekes (2012), essentially new classes of statistical structuresStatistical structure having objective and strong objective estimatesStrong objective estimateObjective estimate of unknown parameters are introduced in a Polish nonlocally compact group admitting an invariant metric and relations between them are studied in this chapter. An example of a weakly separated statistical structureWeakly separated statistical structure is constructed for which a question asking whether there exists a consistent estimateConsistent estimate of an unknown parameterAxiom of Dependence choices (DC) is not solvable within theAxiom of Dependence choices (DC) theory $$ (ZF)~ \& ~(DC)$$ (ZF)&(DC). A question asking whether there exists an objective consistent estimate of an unknown parameter for any statistical structureStatistical structure in a nonlocally compact Polish groupPolish group with an invariant metric when a subjective one exists, is answered positively when there exists at least one such parameter, the preimage of which under this subjective estimateSubjective estimate is a prevalent. These results extend recent results of Pantsulaia and Kintsurashvili (2014). Some examples of objective and strong objective consistent estimatesStrong objective estimate in a compact PolishPolish group group $$\{0; 1\}^N$$ {0;1}N are considered in this chapter. At end of this chapter we present a certain claim for theoretical statisticians in which each consistent estimation with domain in a nonlocally compact Polish groupPolish group equipped with an invariant metric must pass the certification exam on the objectivity before its practical application and we also give some recommendations.

Gogi Pantsulaia

Chapter 6. Why Null Hypothesis Is Rejected for Almost Every Infinite Sample by the Hypothesis Testing of a Maximal Reliability

The notion of Haar null sets was introduced by J. P. R. Christensen in 1973 and reintroduced in 1992 in the context of dynamical systems by Hunt, Sauer, and Yorke. During the last 20 years this notion has been useful in studying exceptional sets in diverse areas. These include analysis, dynamical systems, group theory, and descriptive set theory. In the present chapter their notion of “prevalence” is used in studying structures of domains of some infinite sample statistics and in explaining why null hypothesis is rejected for almost every infinite sample by the hypothesis testing of a maximal reliability.

Gogi Pantsulaia

Backmatter

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