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A Statistics PrimerJohnson, Earl E. 01 March 2011 (has links)
No description available.
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Statistical InferenceChou, 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.
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Statistical debuggingMurphy, 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.
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Fractionation StatisticsWang, 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.
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Statistical extrapolation.Oldson, Dennis Randolph. January 1971 (has links)
No description available.
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Statistical debuggingMurphy, 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.
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The statistics of samplingRichardson, 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).
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The statistics of samplingRichardson, 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).
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Obstetrical statisticsMiller, William Henry January 1888 (has links)
No description available.
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On vital statisticsCuthill, James January 1857 (has links)
No description available.
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