The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Omer Ozturk - One of the best experts on this subject based on the ideXlab platform.
-
Order Statistics Based on a Combined Simple Random Sample from a Finite Population and Applications to Inference
Sankhya A, 2021Co-Authors: Omer Ozturk, Narayanaswamy Balakrishnan, Olena KravchukAbstract:In this paper, we study probability distributions of order statistics from a set obtained by combining several Simple Random Samples (SRS) selected from the same finite population. Each Simple Random Sample is taken using without replacement selection procedure and does not contain any ties. On the other hand, in the combined Sample, the same observation may appear more than once since each SRS is selected from the same finite population. Consequently, the number of the distinct observations in the combined Sample is a discrete Random variable. We provide the probability mass function of this discrete Random variable. Next, using the order statistics in the combined SRSs, we construct confidence intervals for the quantiles and outer-inner confidence intervals for the quantile interval of a finite population. Finally, we also present a prediction interval for a future observation from the same finite population.
-
Constructing quantile confidence intervals using extended Simple Random Sample in finite populations
Statistics, 2019Co-Authors: Omer Ozturk, Narayanaswamy BalakrishnanAbstract:This paper constructs quantile confidence intervals based on extended Simple Random Sample (SRS) from a finite population, where ranks of population units are all known. Extended Simple Random samp...
-
Ratio estimators based on a ranked set Sample in a finite population setting
Journal of the Korean Statistical Society, 2018Co-Authors: Omer OzturkAbstract:Abstract This paper considers the ratio estimator in a finite population setting in a ranked set sampling (RSS) design, where the Sample is constructed either with or without replacement policies. It is shown that the proposed ratio estimator is slightly biased, but the amount of bias is smaller than the amount of bias of a Simple Random Sample (SRS) ratio estimator. For the proposed ratio estimator, the paper provides explicit expressions for its mean square error and precision relative to the other competing estimators. It is shown that the new estimator has a substantial amount of improvement in efficiency with respect to SRS estimator. The proposed estimator is applied to two different finite population settings to estimate population mean.
-
mixture model analysis of partially rank ordered set Samples age groups of fish from length frequency data
Scandinavian Journal of Statistics, 2015Co-Authors: Armin Hatefi, Mohammad Jafari Jozani, Omer OzturkAbstract:type="main" xml:id="sjos12140-abs-0001"> We present a novel methodology for estimating the parameters of a finite mixture model (FMM) based on partially rank-ordered set (PROS) sampling and use it in a fishery application. A PROS sampling design first selects a Simple Random Sample of fish and creates partially rank-ordered judgement subsets by dividing units into subsets of prespecified sizes. The final measurements are then obtained from these partially ordered judgement subsets. The traditional expectation–maximization algorithm is not directly applicable for these observations. We propose a suitable expectation–maximization algorithm to estimate the parameters of the FMMs based on PROS Samples. We also study the problem of classification of the PROS Sample into the components of the FMM. We show that the maximum likelihood estimators based on PROS Samples perform substantially better than their Simple Random Sample counterparts even with small Samples. The results are used to classify a fish population using the length-frequency data.
-
mixture model analysis of partially rank ordered set Samples age groups of fish from length frequency data
Scandinavian Journal of Statistics, 2015Co-Authors: Armin Hatefi, Mohammad Jafari Jozani, Omer OzturkAbstract:We present a novel methodology for estimating the parameters of a finite mixture model (FMM) based on partially rank-ordered set (PROS) sampling and use it in a fishery application. A PROS sampling design first selects a Simple Random Sample of fish and creates partially rank-ordered judgement subsets by dividing units into subsets of prespecified sizes. The final measurements are then obtained from these partially ordered judgement subsets. The traditional expectation–maximization algorithm is not directly applicable for these observations. We propose a suitable expectation–maximization algorithm to estimate the parameters of the FMMs based on PROS Samples. We also study the problem of classification of the PROS Sample into the components of the FMM. We show that the maximum likelihood estimators based on PROS Samples perform substantially better than their Simple Random Sample counterparts even with small Samples. The results are used to classify a fish population using the length-frequency data.
Yan K Liu - One of the best experts on this subject based on the ideXlab platform.
-
Speeding up the asymptotics when constructing one-sided coverage intervals with survey data
METRON, 2010Co-Authors: Phillip S Kott, Yan K LiuAbstract:Coverage intervals for a parameter are frequently derived from a survey Sample by assuming that the Randomization-based parameter estimate is asymptotically normal and that the associated measure of the estimator’s variance is roughly chi-squared. In many situations, however, the size of the Sample and the nature of the parameter being estimated render the conventional Wald technique dubious, especially when a one-sided coverage interval is needed. We will propose a method of coverage-interval construction that “speeds up the asymptotics” so that the resulting one-sided intervals can have much better coverage properties than corresponding Wald intervals. For the important case of a mean computed from a stratified, Simple Random Sample with or without replacement, no model need be assumed. A simulation demonstrates the usefulness of our intervals.
-
one sided coverage intervals for a proportion estimated from a stratified Simple Random Sample
International Statistical Review, 2009Co-Authors: Phillip S Kott, Yan K LiuAbstract:Summary Using an Edgeworth expansion to speed up the asymptotics, we develop one-sided coverage intervals for a proportion based on a stratified Simple Random Sample. To this end, we assume the values of the population units are generated from independent Random variables with a common mean within each stratum. These stratum means, in turn, may either be free to vary or are assumed to be equal. The more general assumption is equivalent to a model-free Randomization-based framework when finite population correction is ignored. Unlike when an Edgeworth expansion is used to construct one-sided intervals under Simple Random sampling, it is necessary to estimate the variance of the estimator for the population proportion when the stratum means are allowed to differ. As a result, there may be accuracy gains from replacing the normal z-score in the Edgeworth expansion with a t-score. Resume Nous developpons des intervalles de confiance unilateraux pour une proportion, lorsqu'un echantillon aleatoire Simple est tire d'une population, en utilisant un developpement en series de Edgeworth pour accelerer la convergence. Pour obtenir ces intervalles, nous supposons que les valeurs des unites de la population sont generees a partir de variables aleatoires independantes avec la meme moyenne a l'interieur de chaque strate. Ces moyennes de strate peuvent, a leur tour, soit etre libres de varier ou etre supposees constantes. L'hypothese la plus generale est equivalente a utiliser un cadre de travail base sur le plan de sondage (ou “Randomization-based”), qui ne necessite donc pas d'hypotheses au sujet d'un modele, et ou l'on ignore la correction pour populations finies. Contrairement au cas dans lequel un developpement en series de Edgeworth est utilise pour construire des intervalles unilateraux sous l'echantillonnage aleatoire Simple, il est necessaire de permettre aux moyennes des strates d'etre differentes les unes des autres lorsqu'on estime la variance de l'estimateur de la proportion dans la population. Par consequent, il peut y avoir des gains de precision lorsqu'on remplace le score z normal dans la serie de Edgeworth par un score t.
Phillip S Kott - One of the best experts on this subject based on the ideXlab platform.
-
Speeding up the asymptotics when constructing one-sided coverage intervals with survey data
METRON, 2010Co-Authors: Phillip S Kott, Yan K LiuAbstract:Coverage intervals for a parameter are frequently derived from a survey Sample by assuming that the Randomization-based parameter estimate is asymptotically normal and that the associated measure of the estimator’s variance is roughly chi-squared. In many situations, however, the size of the Sample and the nature of the parameter being estimated render the conventional Wald technique dubious, especially when a one-sided coverage interval is needed. We will propose a method of coverage-interval construction that “speeds up the asymptotics” so that the resulting one-sided intervals can have much better coverage properties than corresponding Wald intervals. For the important case of a mean computed from a stratified, Simple Random Sample with or without replacement, no model need be assumed. A simulation demonstrates the usefulness of our intervals.
-
one sided coverage intervals for a proportion estimated from a stratified Simple Random Sample
International Statistical Review, 2009Co-Authors: Phillip S Kott, Yan K LiuAbstract:Summary Using an Edgeworth expansion to speed up the asymptotics, we develop one-sided coverage intervals for a proportion based on a stratified Simple Random Sample. To this end, we assume the values of the population units are generated from independent Random variables with a common mean within each stratum. These stratum means, in turn, may either be free to vary or are assumed to be equal. The more general assumption is equivalent to a model-free Randomization-based framework when finite population correction is ignored. Unlike when an Edgeworth expansion is used to construct one-sided intervals under Simple Random sampling, it is necessary to estimate the variance of the estimator for the population proportion when the stratum means are allowed to differ. As a result, there may be accuracy gains from replacing the normal z-score in the Edgeworth expansion with a t-score. Resume Nous developpons des intervalles de confiance unilateraux pour une proportion, lorsqu'un echantillon aleatoire Simple est tire d'une population, en utilisant un developpement en series de Edgeworth pour accelerer la convergence. Pour obtenir ces intervalles, nous supposons que les valeurs des unites de la population sont generees a partir de variables aleatoires independantes avec la meme moyenne a l'interieur de chaque strate. Ces moyennes de strate peuvent, a leur tour, soit etre libres de varier ou etre supposees constantes. L'hypothese la plus generale est equivalente a utiliser un cadre de travail base sur le plan de sondage (ou “Randomization-based”), qui ne necessite donc pas d'hypotheses au sujet d'un modele, et ou l'on ignore la correction pour populations finies. Contrairement au cas dans lequel un developpement en series de Edgeworth est utilise pour construire des intervalles unilateraux sous l'echantillonnage aleatoire Simple, il est necessaire de permettre aux moyennes des strates d'etre differentes les unes des autres lorsqu'on estime la variance de l'estimateur de la proportion dans la population. Par consequent, il peut y avoir des gains de precision lorsqu'on remplace le score z normal dans la serie de Edgeworth par un score t.
Sangeeta Arora - One of the best experts on this subject based on the ideXlab platform.
-
A nonparametric test for a multi-Sample scale problem using ranked-set data
Statistical Methodology, 2013Co-Authors: Anil Gaur, Kalpana K. Mahajan, Sangeeta AroraAbstract:Abstract A nonparametric test for the several-Sample scale problem is proposed, based on ranked-set data. The proposed test has the advantage of not requiring the several distribution functions to have a common median, but rather any common quantile of order α , 0 α 1 (not necessarily 1 / 2 ), which is assumed to be known. It is shown that the new test is uniformly more efficient than its Simple Random Sample analog.
Ece Oral - One of the best experts on this subject based on the ideXlab platform.
-
A robust alternative to the ratio estimator under non-normality
Statistics & Probability Letters, 2011Co-Authors: Evrim Oral, Ece OralAbstract:In sampling theory, the traditional ratio estimator is the most common estimator of the population mean when the correlation between study and auxiliary variables is positively high. We introduce a new ratio-type estimator based on the order statistics of a Simple Random Sample. We show that this new estimator is considerably more efficient than the traditional ratio estimator under non-normality, and remarkably robust to data anomalies such as presence of outliers in data sets.