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Quantile regression with rank-based samplesAyilara, Olawale Fatai 01 November 2016 (has links)
Quantile Regression, as introduced by Koenker, R. and Bassett, G. (1978), provides
a complete picture of the relationship between the response variable and covariates
by estimating a family of conditional quantile functions. Also, it offers a natural
solution to challenges such as; homoscedasticity and sometimes unrealistic normality
assumption in the usual conditional mean regression. Most of the results for quantile
regression are based on simple random sampling (SRS). In this thesis, we study
the quantile regression with rank-based sampling methods. Rank-based sampling
methods have a wide range of applications in medical, ecological and environmental
research, and have been shown to perform better than SRS in estimating several
population parameters. We propose a new objective function which takes into
account the ranking information to estimate the unknown model parameters based
on the maxima or minima nomination sampling designs. We compare the mean
squared error of the proposed quantile regression estimates using maxima (or minima)
nomination sampling design and observe that it provides higher relative e ciency
when compared with its counterparts under SRS design for analyzing the upper
(or lower) tails of the distribution of the response variable. We also evaluate the
performance of our proposed methods when ranking is done with error. / February 2017
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Statistical inference with randomized nomination samplingNourmohammadi, 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.
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