Spelling suggestions: "subject:"confidence intervals"" "subject:"konfidence intervals""
51 
Empirical Likelihood Confidence Intervals for Generalized Lorenz CurveBelingaHill, Nelly E. 28 November 2007 (has links)
Lorenz curves are extensively used in economics to analyze income inequality metrics. In this thesis, we discuss confidence interval estimation methods for generalized Lorenz curve. We first obtain normal approximation (NA) and empirical likelihood (EL) based confidence intervals for generalized Lorenz curves. Then we perform simulation studies to compare coverage probabilities and lengths of the proposed ELbased confidence interval with the NAbased confidence interval for generalized Lorenz curve. Simulation results show that the ELbased confidence intervals have better coverage probabilities and shorter lengths than the NAbased intervals at 100pth percentiles when p is greater than 0.50. Finally, two real examples on income are used to evaluate the applicability of these methods: the first example is the 2001 income data from the Panel Study of Income Dynamics (PSID) and the second example makes use of households’ median income for the USA by counties for the years 1999 and 2006

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Bayesian and pseudolikelihood interval estimation for comparing two Poisson rate parameters using underreported dataGreer, Brandi A. Young, Dean M. January 2008 (has links)
Thesis (Ph.D.)Baylor University, 2008. / Includes bibliographical references (p. 99101).

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Statistical inference for normal means with order restrictions and applications to doseresponse studies /Davis, Karelyn Alexandrea, January 2004 (has links)
Thesis (M.Sc.)Memorial University of Newfoundland, 2004. / Bibliography: leaves 94103.

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Some methods for the analysis of skewed data /Dinh, Phillip V. January 2006 (has links)
Thesis (Ph. D.)University of Washington, 2006. / Vita. Includes bibliographical references (leaves 99105).

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Better Confidence Intervals for Importance SamplingSak, Halis, Hörmann, Wolfgang, Leydold, Josef January 2010 (has links) (PDF)
It is well known that for highly skewed distributions the standard method of using the t statistic for the confidence interval of the mean does not give robust results. This is an important problem for importance sampling (IS) as its final distribution is often skewed due to a heavy tailed weight distribution. In this paper, we first explain Hall's transformation and its variants to correct the confidence interval of the mean and then evaluate the performance of these methods for two numerical examples from finance which have closedform solutions. Finally, we assess the performance of these methods for credit risk examples. Our numerical results suggest that Hall's transformation or one of its variants can be safely used in correcting the twosided confidence intervals of financial simulations.(author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

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An Analysis of Confidence Levels and Retrieval of Procedures Associated with Accounts Receivable ConfirmationsRogers, Violet C. (Violet Corley) 12 1900 (has links)
The study addresses whether differently ordered accounts receivable workprograms and task experience relate to differences in judgments, confidence levels, and recall ability. The study also assesses how treated and untreated inexperienced and experienced auditors store and recall accounts receivable workprogram steps in memory in a laboratory environment. Additionally, the question whether different levels of experienced auditors can effectively be manipulated is also addressed.

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Interval Estimation for Linear Functions of Medians in WithinSubjects and Mixed DesignsBonett, Douglas G., Price, Robert M. 01 May 2020 (has links)
The currently available distributionfree confidence interval for a difference of medians in a withinsubjects design requires an unrealistic assumption of identical distribution shapes. A confidence interval for a general linear function of medians is proposed for withinsubjects designs that do not assume identical distribution shapes. The proposed method can be combined with a method for linear functions of independent medians to provide a confidence interval for a linear function of medians in mixed designs. Simulation results show that the proposed methods have good smallsample properties under a wide range of conditions. The proposed methods are illustrated with examples, and R functions that implement the new methods are provided.

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Interval Estimation for the Ratio of Percentiles from Two Independent Populations.Muindi, Pius Matheka 12 August 2008 (has links) (PDF)
Percentiles are used everyday in descriptive statistics and data analysis. In real life, many quantities are normally distributed and normal percentiles are often used to describe those quantities. In life sciences, distributions like exponential, uniform, Weibull and many others are used to model rates, claims, pensions etc. The need to compare two or more independent populations can arise in data analysis. The ratio of percentiles is just one of the many ways of comparing populations. This thesis constructs a large sample confidence interval for the ratio of percentiles whose underlying distributions are known. A simulation study is conducted to evaluate the coverage probability of the proposed interval method. The distributions that are considered in this thesis are the normal, uniform and exponential distributions.

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Two Essays on HighDimensional Inference and an Application to Distress Risk PredictionZhu, Xiaorui 22 August 2022 (has links)
No description available.

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Exploring Confidence Intervals in the Case of Binomial and Hypergeometric DistributionsMojica, Irene 01 January 2011 (has links)
The objective of this thesis is to examine one of the most fundamental and yet important methodologies used in statistical practice, interval estimation of the probability of success in a binomial distribution. The textbook confidence interval for this problem is known as the Wald interval as it comes from the Wald large sample test for the binomial case. It is generally acknowledged that the actual coverage probability of the standard interval is poor for values of p near 0 or 1. Moreover, recently it has been documented that the coverage properties of the standard interval can be inconsistent even if p is not near the boundaries. For this reason, one would like to study the variety of methods for construction of confidence intervals for unknown probability p in the binomial case. The present thesis accomplishes the task by presenting several methods for constructing confidence intervals for unknown binomial probability p. It is well known that the hypergeometric distribution is related to the binomial distribution. In particular, if the size of the population, N, is large and the number of items of interest k is such that k/N tends to p as N grows, then the hypergeometric distribution can be approximated by the binomial distribution. Therefore, in this case, one can use the confidence intervals constructed for p in the case of the binomial distribution as a basis for construction of the confidence intervals for the unknown value k = pN. The goal of this thesis is to study this approximation and to point out several confidence intervals which are designed specifically for the hypergeometric distribution. In particular, this thesis considers several confidence intervals which are based on estimation of a binomial proportion as well as Bayesian credible sets based on various priors.

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