Global ETD Search

131	A Statistics Primer Johnson, Earl E. 01 March 2011 (has links) No description available. statistics Audiology and Speech-Language Pathology Speech and Hearing Science Speech Pathology and Audiology
132	Statistical Inference Chou, Pei-Hsin 26 June 2008 (has links) In this paper, we will investigate the important properties of three major parts of statistical inference: point estimation, interval estimation and hypothesis testing. For point estimation, we consider the two methods of finding estimators: moment estimators and maximum likelihood estimators, and three methods of evaluating estimators: mean squared error, best unbiased estimators and sufficiency and unbiasedness. For interval estimation, we consider the the general confidence interval, confidence interval in one sample, confidence interval in two samples, sample sizes and finite population correction factors. In hypothesis testing, we consider the theory of testing of hypotheses, testing in one sample, testing in two samples, and the three methods of finding tests: uniformly most powerful test, likelihood ratio test and goodness of fit test. Many examples are used to illustrate their applications. finite population correction factor statistical hypothesis type I error composite hypothesis power Central limit theorem Basu theorem critical value goodness of fit test likelihood ratio test Lehmann-Scheffe theorem complete statistic ancillary statistic P-value type II error Rao-Blackwell theorem simple hypothesis one-sided test two-sided test significance level sufficient statistic alternative hypothesis testing hypothesis null hypothesis rejection region confidence interval confidence level minimum variance unbiased estimator interval estimation uniformly most powerful test Cramer-Rao inequality likelihood function mean square error moment estimator error of estimation point estimation maximum likelihood estimator bias consistent estimator minimal sufficient statistic factorization theorem unbiased estimator statistic most powerful test test statistics Neyman-Pearson lemma decision rule
133	Statistical debugging Murphy, Toriano A. 03 1900 (has links) Software debugging is a time consuming and important step in the development and evolution of software systems. Debugging is a practice that normally gets the least praise but normally requires the most attention and effort. The aim of debugging is find and reduce the number of faults in a program, thereby making a program behave the way it is expected. Even with the advances that have been made with computer speed, graphical user interfaces, networking abilities and storage capabilities the cost debugging remains high. The aim of this thesis is to build on the process of debugging using a statistical approach. Statistical debugging is not a new phenomenon, but a statistical debugging technique has been developed to assist in addressing the difficulties of isolating faults software. The tool developed for debugging by this thesis will save time and effort finding faults thereby saving money.
134	Fractionation Statistics Wang, Baoyong 01 May 2014 (has links) Paralog reduction, the loss of duplicate genes after whole genome duplication (WGD) is a pervasive process. Whether this loss proceeds gene by gene or through deletion of multi-gene DNA segments is controversial, as is the question of fractionation bias, namely whether one homeologous chromosome is more vulnerable to gene deletion than the other. As a null hypothesis, we first assume deletion events, on one homeolog only, excise a geometrically distributed number of genes with unknown mean mu, and these events combine to produce deleted runs of length l, distributed approximately as a negative binomial with unknown parameter r; itself a random variable with distribution pi(.). A biologically more realistic model requires deletion events on both homeologs distributed as a truncated geometric. We simulate the distribution of run lengths l in both models, as well as the underlying pi(r), as a function of mu, and show how sampling l allows us to estimate mu. We apply this to data on a total of 15 genomes descended from 6 distinct WGD events and show how to correct the bias towards shorter runs caused by genome rearrangements. Because of the difficulty in deriving pi(.) analytically, we develop a deterministic recurrence to calculate each pi(r) as a function of mu and the proportion of unreduced paralog pairs. This is based on a computing formula containing nested sums. The parameter mu can be estimated based on run lengths of single-copy regions. We then reduce the computing formulae, at least in the one-sided case, to closed form. This virtually eliminates computing time due to highly nested summations. We formulate a continuous version of the fractionation process, deleting line segments of exponentially distributed lengths in analogy to geometric distributed numbers of genes. We derive nested integrals and discover that the number of previously deleted regions to be skipped by a new deletion event is exactly geometrically distributed. We undertook a large simulation experiment to show how to discriminate between the gene-by-gene duplicate deletion model and the deletion of a geometrically distributed number of genes. This revealed the importance of the effects of genome size N, the mean of the geometric distribution, the progress towards completion of the fractionation process, and whether the data are based on runs of deleted genes or undeleted genes. mathematical model evolution whole genome doubling gene loss theory of runs
135	Statistical extrapolation. Oldson, Dennis Randolph. January 1971 (has links) No description available.
136	Statistical debugging Murphy, Toriano A. January 2008 (has links) (PDF) Thesis (M.S. in Computer Science)--Naval Postgraduate School, March 2008. / Thesis Advisor(s): Auguston, Mikhail. "March 2008." Description based on title screen as viewed on May 5, 2008. Includes bibliographical references (p. 91). Also available in print.
137	The statistics of sampling Richardson, C. H. January 1936 (has links) Thesis (Ph. D.)--University of Michigan, 1927. / Thesis note on label mounted on t.p. Lithoprinted. Includes bibliographical references (p. 35). Sampling (Statistics)
138	The statistics of sampling Richardson, C. H. January 1936 (has links) Thesis (Ph. D.)--University of Michigan, 1927. / Thesis note on label mounted on t.p. Lithoprinted. Includes bibliographical references (p. 35). Sampling (Statistics)
139	Obstetrical statistics Miller, William Henry January 1888 (has links) No description available.
140	On vital statistics Cuthill, James January 1857 (has links) No description available.

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