• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 11
  • 1
  • 1
  • 1
  • 1
  • Tagged with
  • 19
  • 19
  • 7
  • 6
  • 6
  • 5
  • 4
  • 4
  • 3
  • 3
  • 3
  • 2
  • 2
  • 2
  • 2
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

On exceptional sets in the metrical theory of uniform distribution

Nair, R. January 1986 (has links)
No description available.
2

A Bayesian method to improve sampling in weapons testing

Floropoulos, Theodore C. 12 1900 (has links)
Approved for public release; distribution is unlimited / This thesis describes a Bayesian method to determine the number of samples needed to estimate a proportion or probability with 95% confidence when prior bounds are placed on that proportion. It uses the Uniform [a,b] distribution as the prior, and develops a computer program and tables to find the sample size. Tables and examples are also given to compare these results with other approaches for finding sample size. The improvement that can be obtained with this method is fewer samples, and consequently less cost in Weapons Testing is required to meet a desired confidence size for a proportion or probability. / http://archive.org/details/bayesianmethodto00flor / Lieutenant Commander, Hellenic Navy
3

Subset selection based on likelihood from uniform and related populations

Chotai, Jayanti January 1979 (has links)
Let π1,  π2, ... π be k (>_2) populations. Let  πi (i = 1, 2, ..., k) be characterized by the uniform distributionon (ai, bi), where exactly one of ai and bi is unknown. With unequal sample sizes, suppose that we wish to select arandom-size subset of the populations containing the one withthe smallest value of 0i = bi - ai. Rule Ri selects πi iff a likelihood-based k-dimensional confidence region for the unknown (01,..., 0k) contains at least one point having 0i as its smallest component. A second rule, R, is derived through a likelihood ratio and is equivalent to that of Barr and Rizvi (1966) when the sample sizes are equal. Numerical comparisons are made. The results apply to the larger class of densities g(z; 0i) = M(z)Q(0i) iff a(0i) < z < b(0i). Extensions to the cases when both ai and bi are unknown and when 0max is of interest are i i indicated. / digitalisering@umu
4

Characterizations of Distributions by Conditional Expectation

Chang, Tao-Wen 19 June 2001 (has links)
In this thesis, first we replace the condition X ¡Ø y in Huang and Su (2000) by X ¡Ù y and give necessary and sufficient conditions such that there exists a random variable X satisfying that E(g(X)| X ¡Ø y)=h(y) f(y )/ F(y), " y Î CX, where CX is the support of X.Next, we investigate necessary and sufficient conditions such that h(y)=E(g(X) | X ¡Ø y ), for a given function h and extend these results to bivariate case.
5

Maximum spacing methods and limit theorems for statistics based on spacings

Ekström, Magnus January 1997 (has links)
The maximum spacing (MSP) method, introduced by Cheng and Amin (1983) and independently by Ranneby (1984), is a general estimation method for continuous univariate distributions. The MSP method, which is closely related to the maximum likelihood (ML) method, can be derived from an approximation based on simple spacings of the Kullback-Leibler information. It is known to give consistent and asymptotically efficient estimates under general conditions and works also in situations where the ML method fails, e.g. for the three parameter Weibull model. In this thesis it is proved under general conditions that MSP estimates of parameters in the Euclidian metric are strongly consistent. The ideas behind the MSP method are extended and a class of estimation methods is introduced. These methods, called generalized MSP methods, are derived from approxima­tions based on sum-functions of rath order spacings of certain information mea­sures, i.e. the ^-divergences introduced by Csiszår (1963). It is shown under general conditions that generalized MSP methods give consistent estimates. In particular, it is proved that generalized MSP methods give L1 consistent esti­mates in any family of distributions with unimodal densities, without any further conditions on the distributions. Other properties such as distributional robust­ness are also discussed. Several limit theorems for sum-functions of rath order spacings are given, for ra fixed as well as for the case when ra is allowed to in­crease to infinity with the sample size. These results provide a strongly consistent nonparametric estimator of entropy, as well as a characterization of the uniform distribution. Further, it is shown that Cressie's (1976) goodness of fit test is strongly consistent against all continuous alternatives. / digitalisering@umu
6

A proposed algorithm toward uniform-distribution monotone DNF learning

Bi, Wenzhu. January 2004 (has links)
Thesis (M.S.)--Duquesne University, 2004. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p. 24-25) and index.
7

Renewal theory for uniform random variables

Spencer, Steven Robert 01 January 2002 (has links)
This project will focus on finding formulas for E[N(t)] using one of the classical problems in the discipline first, and then extending the scope of the problem to include overall times greater than the time t in the original problem. The expected values in these cases will be found using the uniform and exponential distributions of random variables.
8

Visualizing Peak and Tails to Introduce Kurtosis

Kotz, Samuel, Seier, Edith 01 October 2008 (has links)
This article proposes a simple method to visualize peak and tails in continuous distributions with finite variance. The excess peak and tails areas in unimodal symmetric and nonsymmetric distributions, and the missing area in U-shaped distributions, are identified by comparing the distribution under consideration with the uniform distribution with equal center and variability. Agreement with kurtosis orderings based on the CDFs, and a strong correlation between the total peak and tails area with quantile kurtosis, were found for the distributions examined. The visualization of tails and peak could be used to introduce the notion of kurtosis in undergraduate statistics courses.
9

Normal Numbers with Respect to the Cantor Series Expansion

Mance, Bill 03 August 2010 (has links)
No description available.
10

Multidimensional Khintchine-Marstrand-type Problems

Easwaran, Hiranmoy 29 August 2012 (has links)
No description available.

Page generated in 5.3063 seconds