Search

In this paper we proposed a class of ratio estimators using auxiliary information for the estimation of population mean under stratified sampling. The proposed estimator in this work is based on the Yadav and Baghel (2021) estimator. The expression of the mean square error for this class of estimators is derived and the performance of the proposed estimator is compared with competing estimators. Numerical illustration of efficiency comparison is also worked out in the paper. [ABSTRACT FROM AUTHOR]

In this article, a new discrete distribution called the exponentiated discrete Lindley distribution is derived. The usefulness of this distribution is supported by its ability to analyze different types of count data (over-, under-, equi-dispersed, positively, and negatively skewed). Also, with two parameters, it possesses a bathtub-shaped hazard rate function, which is not the case for well-known discrete distributions. We also discuss some of its important properties with numerical illustrations. Subsequently, the point and interval estimation of the parameters are performed under the classical and Bayesian paradigms. A simulation study is carried out to showcase the numerical illustration of the discussed estimation procedures. To examine the practical applicability, four real datasets are fitted, and the results are fairly compared with other well-known existing discrete models. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new generalized form of Rayleigh distribution which offers a more flexible and reliable model for lifetime data. Several statistical properties of the new distribution are explored. Density expansion, moments, order statistics, information measures, Bonferroni and Lorenz curves, hazard and reverse hazard functions are studied. Furthermore, we obtain model parameters estimation using the maximum likelihood and real data applications which illustrate the performance of the distribution and its excellence over other distributions based on some information criterion are also given. [ABSTRACT FROM AUTHOR]

In this article, we proposed a novel dual to ratio cum product type exponential estimator in stratified random sampling. The bias and mean square error up to the first degree of approximation of the proposed estimator have been obtained. The proposed estimator has been compared with the unbiased estimator in stratified random sampling, combined ratio and product estimators, dual to combined ratio, and product estimators. Our results showed a great improvement in terms of relative efficiency. Also, the results are supported by empirical studies. [ABSTRACT FROM AUTHOR]

In this article we have proposed some new improved ratio estimators based on robust regression that are robust against outliers and provide reliable results even when outliers are present; the properties are also investigated. The proposed class of estimators has been shown to be more effective than the current classes of estimators. An empirical analysis was conducted to see how well the proposed class of estimators compared to others. [ABSTRACT FROM AUTHOR]

This study proposes exponential ratio type estimators of population mean in case of simple random sampling without replacement (SRSWOR) when the auxiliary variable is an attribute. The double sampling version of these estimators is also proposed. Expressions for Bias and mean square error (MSE) have been evaluated. The proposed estimators were found to be more precise and also work as an alternative to different types of estimators as already existing. In order to support the theoretical findings, efficiency of the proposed estimators has also been analyzed numerically by using different population datasets. [ABSTRACT FROM AUTHOR]

The present paper advocates the improved estimation of population mean through a new class of estimators using the known informations on an auxiliary variable under the Simple Random Sampling Scheme. The existing estimators of population mean have been shown as the members of the proposed class. The Bias and Mean Squared Error (MSE) of the suggested class are derived upto the first order of approximation. The values of Bias and the minimum MSE are obtained by optimizing the chracterizing scalar. The MSE of the suggested estimator has also been compared with the MSEs of the existing estimators for the comparison of these estimators. Some of the new members of the suggested family are also presented in the paper which are more efficient than the existing ones emperically which has been shown using a natural population. [ABSTRACT FROM AUTHOR]

In survey sampling, the errors which are mostly studied during estimation are sampling errors. However, the properties of estimators are more influenced by non-sampling errors than sampling errors. This paper addresses the problem of estimating the finite population mean of the study variable y using auxiliary information in sample surveys in the presence of non-response. We have proposed an estimator for estimating the population mean of study variable when the parameter of auxiliary variable x is known. The bias and mean squared error (MSE) of the proposed estimator are obtained to the first degree of approximation. The minimum mean square error of the proposed estimator is also obtained. A Simulation study has been carried out to support the theoretical results. The comparison of the proposed estimator with other estimators is made to show that our proposed estimator is more efficient than the other estimators in terms of percent relative efficiency (PRE). [ABSTRACT FROM AUTHOR]

In this paper, some new functional forms of ratio and product estimators of population mean, namely, logarithmic ratio and product estimators have been introduced. The expressions of biases and mean square errors (MSEs) of these estimators have been obtained up to order . Further, the proposed estimators have been compared with the mean per unit, usual ratio and product estimators, and it has been found that the former are more efficient than the latter under a certain set of conditions. Also, under some practical situations, biases of proposed estimators are less than the corresponding biases of exponential ratio and product type estimators. Moreover, to improve the efficiency of proposed estimators, the transformation have been considered by shifting the origin of auxiliary variable and the optimum transformations have also been found for the proposed estimators for which MSEs of these estimators become minimum. [ABSTRACT FROM AUTHOR]

In this paper we pretend to draw a panoramic view of the basic elements of ratio estimation. In it are presented approaches for extending the classic estimator, generalizing and determining classes of ratio type estimators. [ABSTRACT FROM AUTHOR]

In this article, we have suggested general class of estimators of the finite population mean using an auxiliary variable that is correlated with the variable of interest using ranked set sampling (RSS) method. We also obtained the biases and mean squared errors (MSEs) of various suggested estimators. We have compared newly suggested generalised ratio-cum-product type estimators to others existing estimators and shows that our suggested estimators are better than other existing estimators. This paper also provides the correct expressions of MSE of the estimators due to Mehta and Mandowara (2012, 2016) and Mandowara and Mehta (2013). [ABSTRACT FROM AUTHOR]

In order to propose a statistical methodology that allows to model asymmetric data using the Lévy distribution, a simulation study is presented under nine different scenarios to evaluate the estimation of the parameters of the distribution in the two approaches of statistics (Classical and Bayesian). The probability distributions Log-Normal, Lévy and Lévy Standard were considered to model the behavior of two real data sets with positive asymmetry, finding that the Lévy distribution fitted well to the proposed data set, therefore the Lévy distribution can be considered as a candidate to adjust asymmetric data with the presence of extreme values. [ABSTRACT FROM AUTHOR]

Poverty is a very serious problem and often faced by the countries in the world, especially developing countries. The percentage of poor people in Indonesia reached 11.47 percent in 2013. The seven provinces with the highest poverty in Indonesia are Papua, West Papua, East Nusa Tenggara, Maluku, Gorontalo, Bengkulu and Aceh. This problem is modeled using mixed nonparametric regression of Kernel and Fourier Series. The response variable of this model is percentage of poor people (y), the predictor variables that follow Kernel regression curve are Mean of Years Schooling or MYS (v1) and Literacy Rate or LR (v2), whereas the predictor variable that follow the Fourier Series regression curve are Unemployment Rate or UR (t1). This modeling produces R2 = 62.78%. [ABSTRACT FROM AUTHOR]

This paper is devoted to the analysis of the estimation of the proportion of a sensitive variable. The use of a randomized response (rr) procedure gives confidence to the interviewed that his privacy is protected. We consider that a simple random sampling with replacement design is used for selecting a sample. The behavior of the rr procedure, when ranked set sampling is the design used, is developed under three different ranking criteria. The usual gain in accuracy associated with the use of ranked set sampling is exhibited only by one of the designs. The behavior of the models is illustrated using data provided by a study of samples of persons infected with AIDS. [ABSTRACT FROM AUTHOR]

In this work we develop statistical inference procedures, based on bootstrap and permutation tests, for the European Customer Satisfaction Index (ECSI). The main objective is to fill an existent gap, to the best of our knowledge, in statistical methods for confidence interval estimation of one ECSI, for hypothesis testing comparing two or more ECSIs, and for ranking a set of ECSIs. The procedures proposed is applied to a study of higher education students' satisfaction conducted in Veracruz University (México). The results indicate that the procedures of statistical inference are feasible and straightforward to implement with R. [ABSTRACT FROM AUTHOR]

In survey sampling a very dominating problem is to obtain the better ratio estimators of the population mean and population variance. A very striking question, in every researcher's mind, is that if there are outliers in the data how to estimate the population mean and variance, as outliers mislead the results. So keeping the above problem under consideration, we propose some new modified ratio estimators using robust regression, which are robust against the outliers and give accurate results even in the presence of the outliers; also the properties are studied. It has been shown that the proposed class of estimators is more efficient than the existing classes of estimators. An empirical study has been carried out to examine the merits of the proposed class of estimators over others. [ABSTRACT FROM AUTHOR]

In information theory, Kullback-Leibler divergence measure is a commonly used difference measure that is used for computing the distance between two probability distributions. In this paper, we apply Kullback-Leibler divergence measure between actual and approximate distribution to drive a loss function. We then apply the derived loss function on Exponential distribution to find the Bayes estimate of the parameter ϑ, and compare it with the Bayes estimate obtained using square error loss function. Our comparisons between these two estimates are based on complete, type II censoring and type I censoring data. [ABSTRACT FROM AUTHOR]

The present paper deals with estimation of population mean with more precision by incorporating the auxiliary information, as it has been recognised that incorporation of auxiliary information at design or at estimation stage or at both stages, results in more precision in estimating population parameters of interest. We have proposed the improved family of estimators by incorporating the auxiliary information of conventional and non-conventional location parameters and their linear combinations with correlation coefficient and coefficient of variation for estimating the population mean with more precision. The properties associated with the proposed estimators are assessed by mean square error and bias and compared with the classical and existing estimators. By this comparison we found that our proposed estimators are more efficient than the classical and existing estimators. Numerical illustration is also given in support of the theoretical results. [ABSTRACT FROM AUTHOR]

This article deals with the estimation of population mean under rank set sampling using an improved ratio estimator. The expression for bias and mean square error has been obtained up to first degree of approximation. An efficiency comparison has also been considered for the proposed estimator with the classical ratio estimator under rank set sampling. Finally an empirical study is carried out to judge the performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

In this paper some generalized classes of minimum mean square error estimators of the finite population mean in the presence of a single auxiliary variable are proposed and their efficiencies are compared both theoretically and with numerical illustrations. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of estimating the finite population variance using auxiliary information in presence of measurement errors. We have suggested a class of estimators and its properties are studied under large sample approximation. It has been shown that the usual unbiased estimator and the estimators due to Sharma and Singh [A generalized class of estimators for finite population variance in presence of measurement errors, Journal of Modern Applied Statistical Methods, (2013), 12(2), 231-241.] are members of the proposed class of estimators. An alternative expression of the mean squared error of one the estimator due to Sharma and Singh [A generalized class of estimators for finite population variance in presence of measurement errors, Journal of Modern Applied Statistical Methods, (2013), 12(2), 231-241.] is also provided. The relative performance of various estimators has been examined through an empirical study. [ABSTRACT FROM AUTHOR]

This paper suggests a generalized class of estimators for the population mean of a qualitative study variable in simple random sampling using information on an auxiliary variable. Asymptotic expressions of bias and mean square error of the proposed class of estimators have been obtained. Asymptotic optimum estimator has been investigated along with its approximate mean square error. It has been shown that the proposed generalized class of estimators are more efficient than all the estimators considered by Singh et al. (2010) in case of a qualitative study variable. In addition theoretical findings are supported by an empirical study based on real population to show the superiority of the constructed estimators over others. [ABSTRACT FROM AUTHOR]

A class of estimators is determined. It contains important families of ratio estimators. The belonging to it is characterized by means of a parametric vector. It allows identifying that more than a dozen of estimators are particular cases. Their behavior when ranked set sampling is used is established by fixing the gains in accuracy of them. Numerical experiments establish which is the performance of them in four applications and by generating normal, exponential and uniform populations. [ABSTRACT FROM AUTHOR]

In this paper some generalized classes of minimum mean square error estimators of the finite population mean in the presence of a single auxiliary variable are proposed and their efficiencies are compared both theoretically and with numerical illustrations. [ABSTRACT FROM AUTHOR]

In this paper, improved ratio and product type estimators have been developed for estimating the finite population mean of the study variable using auxiliary information in simple random sampling (SRS). The expressions for the bias and mean square error (MSE) of the proposed estimators are obtained under first order of approximation. Theoretical and empirical studies have been done to demonstrate the efficiencies of the proposed estimators over other well known estimators. [ABSTRACT FROM AUTHOR]

This paper proposes classes of estimators for the mean of sensitive variable utilizing information on a non - sensitive auxiliary variable. Expressions for the biases and mean square errors of the suggested estimators correct up to first order of approximation are derived. It has been shown that the suggested new class of estimators based on the known population mean X and variance Sx 2 of the auxiliary variable X are better than conventional unbiased estimators which do not utilize the auxiliary information, Sousa et al.'s (2010) ratio estimator and Gupta et al.'s (2012) regression estimator under a very realistic condition. [ABSTRACT FROM AUTHOR]

Taking the clue from the pioneer work of Hussain et al.'s (2014) we have suggested a new stratified randomized response model. The properties of the suggested stratified randomized response model have been studied under proportional and "Neyman" allocations. The study has been also carried out using modified Neyman allocation in the presence of crude prior estimates. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

In this paper a new form of ratio estimator using the linear combination of Median and Quartile Deviation of auxiliary variable under rank set sampling has been suggested. We have proved theoretically that the proposed estimator is more efficient than the classical estimator of ranked set sampling. The results are supported by empirical study also. [ABSTRACT FROM AUTHOR]

This paper suggests some generalized classes of modified ratio, regression-cum-ratio and exponential ratio type estimators for finite population mean of the study variable utilizing the information on two auxiliary variables in stratified random sampling. The expressions for bias and mean square error of proposed class of estimators have been derived and compare with those of existing estimators. An empirical study is carried out to demonstrate the efficiency of the proposed class of estimators over others and it is found that the empirical results support the theoretical study. [ABSTRACT FROM AUTHOR]

The problem of estimating the population mean is considered by McIntyre (1952). A new sampling method is suggested, namely; ranked set sampling (RSS) as efficient method compared to the well known simple random sampling (SRS) method. In the last years many authors suggested different modifications of the RSS and used it in wide applications. In this paper, a literature review of the RSS method is presented as well as some its modifications and applications are provided. [ABSTRACT FROM AUTHOR]

To be able to resolve tomographic inversion problems in travel time, it is necessary the construction of distance matrices containing information of survey array geometry, the discretized domain shape and the distance traveled by rays in each area elements that compose it. Domain considered are generally rectangular for ease of geometric resolution. In this work is circular due to the need to carry out investigation in columns of this shape. Distance matrices are used to solve the linear system, which leads to the determination of velocity values in each element of the domain. Taking in mind discretization, there are several possibilities for the construction of arrays of pixels: may those be defined in one quadrant and obtaining the rest by symmetry; build arrays using any size pixels independent of the quadrant, and finally construct distance matrices with square pixels. Each option will provide access to new research possibilities. In this work an original procedure was developed for the construction of matrices with elements of the last type. The procedure was validated with simulated data using the damped least squares solution (DLSS) method, which demonstrated the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Binary logistic regression as an instrument for the prediction of immunological deterioration from nutritional indicators in persons infected with HIV-AIDS. In the evolution of infection with HIV diverse markers of progression have been used, of immunological, virological and genetic type. However, the significance of nutritional indicators for prognosis has been insufficiently studied. The objective of this investigation was to use binary logistic regression as a tool to find a mathematical model which would allow to predict the counting of CD4+ cells in persons infected with HIV-AIDS after a year's follow-up. 118 persons with HIV-AIDS were studied (initial time and after a year of evolution) which were classified in four subgroups according to the use or not use of antirretrovirals and the counting of CD4 at the initial time. Nutritional, biochemical and anthropometric variables were used, as well as the gradients between both observations. Binary logistic regression was applied, having as dependent variable the counting of CD4 as an immunological criterion of antirretroviral therapy. The logistical models found were adjusted according to the subgroup they belonged to, and they were compared to one another according to the goodness of fit and accuracy. The step-by-step method chose only the gradient of body mass index (IMC) as independent variable. The adjustment of the model allowed to propose a mathematical equation by subgroup in order to predict the need of antirretroviral therapy after a year. The groups which had initial values of CD4 < 350, even with an increase in IMC ran the risk of keeping immunological deterioration after a year. All models showed specificity values higher than those of sensitiveness, though models II and III were the best, as proved by the probabilities shown in ROC curves. The conclusion is that the IMC gradient in a year of evolution might be used in a logistical mathematical model to predict the need of antirretroviral therapy in individuals with HIV-AIDS. [ABSTRACT FROM AUTHOR]

In this paper an alternative ratio type exponential estimator is suggested and is compared with Bahl and Tuteja's ratio type exponential estimator and classical ratio estimator as regards bias and mean square error with large sample approximations both theoretically and with numerical illustration. [ABSTRACT FROM AUTHOR]

In this paper a system consisting of independently operating n-machines is considered, where each machine follows a stochastic process of deterioration, which is associated with a cost function. It is assumed that the system is observed at discrete time and the objective function is the total expected cost. Also, it is considered that the horizon of the problem is random with an infinite support. In this case, the optimal replacement problem with a random horizon is modelled as a non-homogeneous optimal control problem with an infinite horizon and solved by means of the rolling horizon procedure. Then, a replacement policy is provided, which approximate the optimal solution. Finally, a numerical example through a program in Maple is presented. [ABSTRACT FROM AUTHOR]

In this paper different modified ratio and product type estimators are considered. Their biases and mean square errors are compared and necessary conditions are derived Further, numerical illustrations are provided with the help of some natural population to compare their biases and efficiencies. [ABSTRACT FROM AUTHOR]

In this work we have collected estimators of the population proportion through the use of auxiliary information under simple random sampling. We analyzed the main properties of these estimators. Theorethical properties suggest that the estimators with use of auxiliary information can outperform alternative methods, and the results derived from a Monte Carlo simulation study support this view. Has made an application on a population of Risk of Acidication in Lakes in the Northeastern states of the U.S. conducted by the Environmental Monitoring and Assessment Program of the US Environmental Protection Agency. In the application we have compared the different estimators each other with the standard stimators. [ABSTRACT FROM AUTHOR]

In the paper the results of the combined strategy formed by Robust Principal Component Analysis and CUR matrix decomposition are applied to a practical problem for adjusting a regression model by using Partial Least Squares (PLS). The conclusions concerning their advantages of such an approach as well information about the adjusted models are given. [ABSTRACT FROM AUTHOR]

In a classic problem of competing risks, estimates of diverse rates are carried out starting from suppositions about the causes that compete. These suppositions have included essentially the independence among the competing causes. The present work intends to relax these suppositions to more general situations. It is demonstrated that the Net Rate can be estimated from observed data in the presence of Competing Risks with certain type of dependence. Generalizations of the Actuarial estimator for the Net Rate are suggested for the two classes of dependencies studied. By means of simulations, the proposed estimators and the one reported in the literature for independent competing risks are compared, and the results confirm the fact that the new estimators can be used in the circumstances defined to estimate the probabilities of survival. [ABSTRACT FROM AUTHOR]

The existence of missing data in sample surveys reduces the efficiency of the estimators. This paper is concerned with the use of imputation for dealing with this problem. The mean imputation and the ratio methods for the median estimator are used for developing alternatives with respect to the full response case in ranked set sampling. [ABSTRACT FROM AUTHOR]

The technique of Randomized Response has been introduced to reduce the risk of evasive answer or no-response in survey samplings of sensitive issues to estimate the total of individuals with that sensitive characteristic. We also know that highly efficient strategies of estimation require strong auxiliary information. Hence, in this article we obtain and compare two estimators: The Rao-Hartley-Cochran (RHC) estimator with Warner's model and the RHC estimator with Greenberg's model, both in a finite population setting. We will show via simulation that ŧA1,RHC,π the RHC π-estimator in Warner's model is less efficient than ŧA1,RHC,π, the RHC π-estimator in Greenberg's model. [ABSTRACT FROM AUTHOR]

This paper introduces a general class of Inclusion Probability Proportional to Size (IPPS) sampling schemes for selecting two units from a finite population. All IPPS sampling schemes, identified as particular members of this class, possess some desirable properties with regard to the inclusion probabilities, and provide unbiased and non-negative variance estimators under Horvitz-Thomson (HT) model. [ABSTRACT FROM AUTHOR]

Double robust extreme ranked set sampling (DRERSS) and its properties for estimating the population median are suggested. The performance of DRERSS with respect to simple random sampling (SRS), ranked set sampling (RSS) and extreme ranked set sampling (ERSS) schemes is considered. Real data set that consist of heights of 346 students are used to evaluate the DRERSS method. It is found that when the underlying distribution is symmetric, the DRERSS estimators are unbiased of the population median. When the sample size is odd, DRERSS is efficient than SRS, ERSS, and RSS. When the sample size is even, DRERSS is more efficient than RSS and ERSS if the underlying distribution is symmetric, and for asymmetric distributions, DRERSS is more efficient than RSS and ERSS for k > 0 based on the same number of measured units. [ABSTRACT FROM AUTHOR]

This paper analyses the use of ranked set sampling procedures for obtaining the sub-sample from the set of non- respondents. The first visit may serve for ranking accurately the sub-sampled non-respondents. The usual ranked set sample (rss) design and two variations of it : extreme-rss and median-rss are used for developing estimators of the population mean. Their expected variances and biases are obtained. A Monte Carlo experiment is developed for evaluating the behavior of the estimators. The use of rss appears as the best alternative. [ABSTRACT FROM AUTHOR]

In this work simultaneous confidence intervals for the variance components in the two-way balanced crossed random effects model with interaction have been derived under the usual assumptions of normality and independence of random effects. The intervals are conservative in the sense that the true confidence coefficient is as large as preassigned value. The formulas are illustrated using published data with SAS outputs. [ABSTRACT FROM AUTHOR]

We consider the use of a finite population model for estimating the difference of the means of two variables under a mechanism for missing observations. The information available on the two variables is used for imputing the corresponding missing data. The proposed estimator is characterized by means of the expected squared error. Data from an environmental study are used for illustrating the performance of the estimator. [ABSTRACT FROM AUTHOR]

In this paper, modified ratio estimators of the population mean of the variable of interest are suggested involving the first or third quartiles of an auxiliary variable that is correlated with the variable of interest. The newly suggested estimators are investigated under simple random sampling (SRS) and ranked set sampling (RSS) methods. It is found that these estimators are approximately unbiased and the RSS estimators are more efficient than those based on SRS method for the same quartile and sample size. Also, it is found that, when the two quartiles are compared, estimators based on the third quartile are more efficient than the first quartile. [ABSTRACT FROM AUTHOR]

This paper deals with the solution of the estimation of the mean using product type estimators when missing observations are present in a survey sampling. The Non Response striatum approach is considered and two estimators are developed. Imputation methods are also developed and two predictors are proposed. They are compared using Monte Carlo experiments. [ABSTRACT FROM AUTHOR]

This paper is devoted to the study of the estimation of the population mean using of product estimator under ranked set. Strategies for different alternative estimators are developed and compared with theirs simple random sampling with replacement counterparts. The sample errors of them are compared analytically. [ABSTRACT FROM AUTHOR]

The majority of the developments in a classic problem of competing risks have consisted in, based on the assumption of independence between all event types, make estimations to several rates. In this paper, we propose an adjusted Life-Table estimator in the context of non-independent competing risks. We proof that the proposed estimator is maximum pseudo-likelihood. We compare the adjusted and classic estimators by simulation study. It confirmed that de adjusted estimator provides closer estimations to the real rate than the classic estimator. [ABSTRACT FROM AUTHOR]

The article presents a study about the concept of Ranked Set Sampling (RSS) as a method in collecting samples. In this method, random selection is made with the replacement of samples which are ranked. Each of the order statistics is observed once and can either be repeated or not. RSS produces the study of the design effect in non parametric inference.