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11	Statistical inference with randomized nomination sampling Nourmohammadi, Mohammad 08 1900 (has links) In this dissertation, we develop several new inference procedures that are based on randomized nomination sampling (RNS). The first problem we consider is that of constructing distribution-free confidence intervals for quantiles for finite populations. The required algorithms for computing coverage probabilities of the proposed confidence intervals are presented. The second problem we address is that of constructing nonparametric confidence intervals for infinite populations. We describe the procedures for constructing confidence intervals and compare the constructed confidence intervals in the RNS setting, both in perfect and imperfect ranking scenario, with their simple random sampling (SRS) counterparts. Recommendations for choosing the design parameters are made to achieve shorter confidence intervals than their SRS counterparts. The third problem we investigate is the construction of tolerance intervals using the RNS technique. We describe the procedures of constructing one- and two-sided RNS tolerance intervals and investigate the sample sizes required to achieve tolerance intervals which contain the determined proportions of the underlying population. We also investigate the efficiency of RNS-based tolerance intervals compared with their corresponding intervals based on SRS. A new method for estimating ranking error probabilities is proposed. The final problem we consider is that of parametric inference based on RNS. We introduce different data types associated with different situation that one might encounter using the RNS design and provide the maximum likelihood (ML) and the method of moments (MM) estimators of the parameters in two classes of distributions; proportional hazard rate (PHR) and proportional reverse hazard rate (PRHR) models. Randomized nomination sampling Confidence interval Tolerance interval Finite population Infinite population Proportional hazard rate model Maximum likelihood Method of moment
12	Filas estocásticas com fonte finita: uma abordagem alternativa / Finite source stochastic queue: an alternative approach Algisi, Renata 05 February 1996 (has links) Uma série de problemas de filas em sistemas de transportes com picos de tráfego, ou um número finito de elementos no sistema, são usualmente representados pelo modelo de fonte infinita, dadas as dificuldades de utilização do modelo exato de fonte finita. Este trabalho apresenta uma solução alternativa baseada no cálculo de um limitante superior para as probabilidades de equilíbrio do modelo exato de fonte finita, e compara as medidas de desempenho dos sistemas calculadas pelos dois modelos. Mostra-se que para índices de congestionamento menores que um, as diferenças entre estas medidas são tanto menores quanto menor for este índice. A partir destes resultados, compara-se as medidas de desempenho do modelo aproximado proposto com as do modelo de população infinita, para diferentes tamanhos de população e números de servidores. Conclui-se que os modelos conduzem a resultados numéricos muito próximos para uma ampla variação do índice de congestionamento, e que estes resultados são tão melhores quanto maior for o número de servidores no sistema e o número de elementos na população. São também apresentados três estudos de casos comparando os resultados induzidos pelos modelos exato, proposto e usual de fonte infinita, que ilustram a aplicabilidade prática dos resultados deste trabalho em sistemas de transportes. / A set of stochastic queueing problems in transportation systems with traffic peaks, or a finite number of elements in the system, are usually represented by the infinite source model, due to the difficulties of applying the exact finite source model. This study presents an alternative solution based on the upper bound values of the equilibrium probabilities of the exact finite source model, and compares the performance measurements of the two models. It is shown that for congestion factors below one, the smaller the value of the congestion factor, the smaller is the difference between the models. Based on this results the measures of performance of the proposed aproximate model are compared with the results of the usual infinite source model for different population sizes and number of servers. It is concluded that the models lead to very close numerical results for a wide range of congestion factors of the system and that these results are the better the larger is the number of servers and the number of elements in the system. Three case studies ilustrating the pratical applicability of the results of this study to transportation systems are also presented. Fila com fonte finita Finite population systems Finite source queue Processos estocásticos Queueing theory Sistemas com população finita Stochastic process Teoria das filas
13	Filas estocásticas com fonte finita: uma abordagem alternativa / Finite source stochastic queue: an alternative approach Renata Algisi 05 February 1996 (has links) Uma série de problemas de filas em sistemas de transportes com picos de tráfego, ou um número finito de elementos no sistema, são usualmente representados pelo modelo de fonte infinita, dadas as dificuldades de utilização do modelo exato de fonte finita. Este trabalho apresenta uma solução alternativa baseada no cálculo de um limitante superior para as probabilidades de equilíbrio do modelo exato de fonte finita, e compara as medidas de desempenho dos sistemas calculadas pelos dois modelos. Mostra-se que para índices de congestionamento menores que um, as diferenças entre estas medidas são tanto menores quanto menor for este índice. A partir destes resultados, compara-se as medidas de desempenho do modelo aproximado proposto com as do modelo de população infinita, para diferentes tamanhos de população e números de servidores. Conclui-se que os modelos conduzem a resultados numéricos muito próximos para uma ampla variação do índice de congestionamento, e que estes resultados são tão melhores quanto maior for o número de servidores no sistema e o número de elementos na população. São também apresentados três estudos de casos comparando os resultados induzidos pelos modelos exato, proposto e usual de fonte infinita, que ilustram a aplicabilidade prática dos resultados deste trabalho em sistemas de transportes. / A set of stochastic queueing problems in transportation systems with traffic peaks, or a finite number of elements in the system, are usually represented by the infinite source model, due to the difficulties of applying the exact finite source model. This study presents an alternative solution based on the upper bound values of the equilibrium probabilities of the exact finite source model, and compares the performance measurements of the two models. It is shown that for congestion factors below one, the smaller the value of the congestion factor, the smaller is the difference between the models. Based on this results the measures of performance of the proposed aproximate model are compared with the results of the usual infinite source model for different population sizes and number of servers. It is concluded that the models lead to very close numerical results for a wide range of congestion factors of the system and that these results are the better the larger is the number of servers and the number of elements in the system. Three case studies ilustrating the pratical applicability of the results of this study to transportation systems are also presented. Fila com fonte finita Processos estocásticos Sistemas com população finita Teoria das filas Finite population systems Finite source queue Queueing theory Stochastic process
14	Horvico ir Tompsono įvertinio dispersijos vertinimas / Estimation of the variance of the Horvitz & Thompson estimator Žakienė, Inesa 13 August 2012 (has links) Šiame magistro diplominiame darbe, naudojant skirtingas atstumo funkcijas ir kalibravimo lygtis, išvedami Horvico ir Tompsono įvertinio dispersijos įvertinių svoriai. Tokiu būdu, sukonstruojami aštuoni nauji Horvico ir Tompsono įvertinio dispersijos įvertiniai. Naudojant Teiloro ištiesinimo metodą pateikiamos sukonstruotų įvertinių apytikslės dispersijos ir pasiūlyti šių dispersijų įvertiniai. Be to, darbe atliekamas matematinis modeliavimas, kurio eksperimentai atlikti naudojant darbo autorės sukurtas MATLAB programas. Matematinio modeliavimo tikslas - naujus įvertinius palyginti tarpusavyje ir su standartiniu įvertiniu. Tiriama, kaip įvertinių tikslumas priklauso nuo pasirinkto imties plano. / In this master's graduation work, the weights of estimators of Horvitz & Thompson estimator of variance are defined by using some different distance function and calibration equations. In such a way, the new eight estimators of Horvitz & Thompson estimator of variance were constructed. Using the Taylor linearization method the approximate variances of the constructed estimators were derived. The estimators of the variances of these estimators are proposed as well. Also we perform here a mathematical modeling using MATLAB program. The aim of this mathematical modeling is to compare the new estimators with each other and with a standard one. We analyze also how the accuracy of estimators depends of selected sampling design. Mathematics Apytikslė dispersija Baigtinė populiacija Horvico ir Tompsono įvertinys Kalibruotasis įvertinys Sluoksninis ėmimas Approximate variance Finite population Horvitz & Thompson estimator Calibrated estimator Stratified sampling design
15	On Methods for Real Time Sampling and Distributions in Sampling Meister, Kadri January 2004 (has links) This thesis is composed of six papers, all dealing with the issue of sampling from a finite population. We consider two different topics: real time sampling and distributions in sampling. The main focus is on Papers A–C, where a somewhat special sampling situation referred to as real time sampling is studied. Here a finite population passes or is passed by the sampler. There is no list of the population units available and for every unit the sampler should decide whether or not to sample it when he/she meets the unit. We focus on the problem of finding suitable sampling methods for the described situation and some new methods are proposed. In all, we try not to sample units close to each other so often, i.e. we sample with negative dependencies. Here the correlations between the inclusion indicators, called sampling correlations, play an important role. Some evaluation of the new methods are made by using a simulation study and asymptotic calculations. We study new methods mainly in comparison to standard Bernoulli sampling while having the sample mean as an estimator for the population mean. Assuming a stationary population model with decreasing autocorrelations, we have found the form for the nearly optimal sampling correlations by using asymptotic calculations. Here some restrictions on the sampling correlations are used. We gain most in efficiency using methods that give negatively correlated indicator variables, such that the correlation sum is small and the sampling correlations are equal for units up to lag m apart and zero afterwards. Since the proposed methods are based on sequences of dependent Bernoulli variables, an important part of the study is devoted to the problem of how to generate such sequences. The correlation structure of these sequences is also studied. The remainder of the thesis consists of three diverse papers, Papers D–F, where distributional properties in survey sampling are considered. In Paper D the concern is with unified statistical inference. Here both the model for the population and the sampling design are taken into account when considering the properties of an estimator. In this paper the framework of the sampling design as a multivariate distribution is used to outline two-phase sampling. In Paper E, we give probability functions for different sampling designs such as conditional Poisson, Sampford and Pareto designs. Methods to sample by using the probability function of a sampling design are discussed. Paper F focuses on the design-based distributional characteristics of the π-estimator and its variance estimator. We give formulae for the higher-order moments and cumulants of the π-estimator. Formulae of the design-based variance of the variance estimator, and covariance of the π-estimator and its variance estimator are presented. Mathematical statistics Finite population sampling inferential issues real time sampling sequential sampling methods negative sampling correlations model-design-based inference Matematisk statistik Mathematical statistics Matematisk statistik
16	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

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