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In continuous search for a better method of estimating the population mean in double sampling techniques, with advantageous properties than some existing ones and to address the problem of efficiency thereon, a generalized efficient family of ratio estimator of population mean under double sampling techniques is suggested using information on a single supplementary variable. Members of the suggested family of estimator were obtained by modifying the values of the scalars associated with the suggested estimator. These estimator and its members were then transformed to an expanded form, from where their properties such as Biases, relative biases, Mean Square Errors (MSEs), and Optimal Mean Square Errors (OMSEs) were derived to the second order of Taylor series approximation. Theoretical underpinnings and conditions for measuring efficiency of an estimator over its competitor estimators were established and validated with some natural population data sets. The results showed that the generalized efficient family of ratio estimators in double sampling techniques produced smaller MSEs which is an indicator of appreciable gain in efficiency and dominance over some existing ratio estimators and were therefore deemed to provide a better substitute whenever priority is placed on efficiency. [ABSTRACT FROM AUTHOR]

In this paper, separate and combined ratio type estimators have been proposed in presence of non-response for estimating the population mean under stratified random sampling when the non-response occurs both on study and the auxiliary variables and the population mean of the auxiliary variable is unknown. The expressions for the biases and mean square errors (MSEs) of the proposed estimators have been derived to the first order of approximation. The proposed estimators have been compared with the other existing estimators using MSE criterion, and the condition under which the proposed estimators perform better than existing estimators have been obtained. In addition to the theoretical research, an empirical study was conducted. [ABSTRACT FROM AUTHOR]

To estimate the population mean when sampling a heterogeneous population and in the absence of a priori information on auxiliary variables, exponential-ratio multivariate estimators are associated under double stratified sampling with two auxiliary variables. Their biases and mean square errors are expressed and simulated. These mean square errors are smaller (the efficiencies are higher) than those of the sample mean estimator and those of other ratio estimators when the correlation between the study and the auxiliary variables exceeds 0.1 in absolute value. In particular, the proposed estimators are more efficient for low correlations between the study and the auxiliary variables. The gain in efficiency reaches a factor of 230.4% on an empirical dataset where the study variable is weakly correlated with each of the two auxiliary variables, and 182.1% on another empirical dataset where it is strongly correlated. [ABSTRACT FROM AUTHOR]

The performance of a population mean estimator can be improved by transformation techniques using a subset of the population data. An improved family of estimators for population mean has been proposed under double sampling using a transformed auxiliary variable. The biases and mean square errors of the proposed family of estimators up to the first order of approximation have been investigated. Simulation studies and an application to fine particulate matter in Chiang Rai, Thailand, are used to study the efficiency of the proposed estimators. The results from an application to air pollution in Chiang Rai showed that the proposed estimators gave smaller biases, by at least a half of the existing ones, and gave at least four times less mean square error than the existing ones. [ABSTRACT FROM AUTHOR]

This paper proposes a side-sensitive group runs double sampling (SSGRDS) chart to detect shifts in the process mean. It improves the side-sensitive group runs chart proposed by Gadre and Rattihalli. The implementation of the SSGRDS chart is explained. The newly developed SSGRDS chart is compared with the synthetic, double sampling, synthetic double sampling, side-sensitive group runs and exponentially weighted moving average charts, in terms of the zero-state and steady-state average number of observations to signal (ANOS) and expected average number of observations to signal (EANOS). The zero-state and steady-state ANOS (ssANOS) and EANOS results reveal that the optimal SSGRDS chart generally performs well for detecting small and large mean shifts, from an overall perspective, compared with the optimal versions of other competing charts. This article provides tables of optimal charting parameters to facilitate the design of the SSGRDS chart. From these tables, the user can directly determine the optimal charting parameters for (i) minimising the standardised mean shift , based on different combinations of in-control ANOS and in-control average sample size , or (ii) minimising an overall range of shifts , based on different in-control EANOS and ASS0 combinations. [ABSTRACT FROM AUTHOR]

Variance is one of the most vital measures of dispersion widely employed in practical aspects. A commonly used approach for variance estimation is the traditional method of moments that is strongly influenced by the presence of extreme values, and thus its results cannot be relied on. Finding momentum from Koyuncu's recent work, the present paper focuses first on proposing two classes of variance estimators based on linear moments (L-moments), and then employing them with auxiliary data under double stratified sampling to introduce a new class of calibration variance estimators using important properties of L-moments (L-location, L-cv, L-variance). Three populations are taken into account to assess the efficiency of the new estimators. The first and second populations are concerned with artificial data, and the third populations is concerned with real data. The percentage relative efficiency of the proposed estimators over existing ones is evaluated. In the presence of extreme values, our findings depict the superiority and high efficiency of the proposed classes over traditional classes. Hence, when auxiliary data is available along with extreme values, the proposed classes of estimators may be implemented in an extensive variety of sampling surveys. [ABSTRACT FROM AUTHOR]

Calibration approach is becoming more important in survey sampling. Calibration estimation helps in improving the estimates of population parameters by making use of auxiliary information. This paper proposes a new calibration estimator for estimating the population mean in the stratified random sampling with a set of new calibration constraints using known coefficient of variation of the auxiliary variable. The result has also been extended to the case of double sampling in stratified sampling. The proposed estimator has been compared with the estimator developed by Tracy et al. (2003) with the help of simulation study on real datasets. [ABSTRACT FROM AUTHOR]

Calibration approach provides efficient estimators of population parameters utilizing auxiliary information. In this paper, a new calibration estimator for population mean has been proposed in stratified random sampling using known coefficient of skewness of the auxiliary variable in defining the calibration constraint. The results so obtained are also extended to stratified double sampling case. A simulation study has also been carried out on real as well as artificial datasets generated from various probability distributions like Gamma, Chi-square and Weibull distributions for various values of the parameters. The proposed estimators are compared with the estimator developed by Tracy et al. (2003) and found more efficient for all considered datasets. [ABSTRACT FROM AUTHOR]

In this article, we propose a method of planning and determining the optimum parameters of a SkSP-R skip-lot sampling plan by using the attribute double sampling plan as the reference plan. The SkSP-R plan is a new type of skip-lot sampling plan which has a provision for re-inspecting the submitted lots. The optimal plan parameters of the suggested sampling plan are estimated with the target that the average sample number be minimized and satisfying both the specified producer's as well as the consumer's risks simultaneously. In order to obtain the optimum parameters, tables are also built for different combinations of the acceptable quality level and the limiting quality level in conjunctions with different producer's and consumer's risks. An illustrative example is provided for the implementation of the suggested plan. The advantages of the suggested plan over the existing conventional sampling plans and other existing skip-lot sampling plans are also described. [ABSTRACT FROM AUTHOR]

The present article deals with the estimation of mean number of respondents who possess a rare sensitive character in presence of known and unknown proportion of a rare unrelated non-sensitive attribute by using the Poisson probability distribution in stratified random sampling as well as in stratified random double sampling. The variance of rare sensitive character is also derived under proportional and optimal allocation methods in stratified random sampling when stratum sizes are known and unknown. The properties of the suggested estimation procedures have been deeply examined. The proposed model is found to be dominant over Lee et al. [Estimation of a rare sensitive attribute in a stratified sample using Poisson distribution. Statistics. 2013;47:575-589] model. Numerical illustrations are presented to support the theoretical results. Results are analysed and suitable recommendations are put forward to the survey practitioners. [ABSTRACT FROM AUTHOR]

Abstract: In this article, two adaptive multivariate charts, which combine the double sampling (DS) and variable sampling interval (VSI) features, called the adaptive multivariate double sampling variable sampling interval T2 (AMDSVSI T2) and the adaptive multivariate double sampling variable sampling interval combined T2 (AMDSVSIC T2) charts, are proposed. The real purpose of using the proposed charts is to provide flexibility by enabling the sampling interval length of the DS T2 chart to be varied so that the chart's sensitivity can be enhanced. The fundamental difference between the two proposed charts is that when a second sample is taken, the AMDSVSI T2 chart uses the information of the combined sample mean vectors while the AMDSVSIC T2 chart uses the information of the combined T2 statistics, in deciding about the process status. This research is motivated by existing combined DS X ¯ and VSI X ¯ charts in the literature, which show convincing performance improvement over the standard DS X ¯ chart. Consequently, it is believed that adopting this existing approach in the multivariate case will enable superior multivariate DS charts to be proposed. Numerical results show that the proposed charts outperform the existing standard T2 and other adaptive multivariate charts, in detecting shifts in the mean vector, for the zero‐state and steady‐state cases. The performances of both charts when the shift sizes in the mean vector are unknown are also measured. The application of the AMDSVSI T2 chart is illustrated with an example. [ABSTRACT FROM AUTHOR]

A side-sensitive modified group runs double sampling (SSMGRDS) chart for detecting mean shifts is proposed. The average number of observations to signal (ANOS) and expected average number of observations to signal (EANOS) performances of the SSMGRDS, , double sampling (DS), side-sensitive group runs double sampling (SSGRDS), side-sensitive modified group runs (SSMGR) and exponentially weighted moving average (EWMA) charts are compared. For the zero-state case, the SSMGRDS chart outperforms all charts under comparison, for all sizes of mean shifts. For the steady-state case, the EWMA chart prevails for small and moderate mean shifts but the SSMGRDS chart prevails for large shifts. [ABSTRACT FROM AUTHOR]

The present investigation deals with the problem of estimation of population mean in two-phase (double) sampling. Utilizing information on two auxiliary variables, one chain exponential ratio and regression type estimator has been proposed and its properties are studied under two different structures of twophase sampling. To make the estimator practicable, unbiased version of the proposed strategy has also been developed. The dominance of the suggested estimator over some contemporary estimators of population mean has been established through numerical illustrations carried over the data set of some natural population and artificially generated population. Categorization of the dominance ranges of the proposed estimation strategies are deployed through defuzzification tools, which are followed by suitable recommendations. [ABSTRACT FROM AUTHOR]

A combined double sampling and variable sampling interval (DSVSI) np chart is investigated in this study. The optimal design of the DSVSI np chart is based on minimizing the out-of-control average time to signal. From the numerical results, the DSVSI np chart performs reasonably well in comparison with the standard np chart, double sampling np chart, synthetic double sampling np chart, and other existing np type control charts for detecting increases in the process of fraction non conforming, based on the zero-state case. An example is provided to illustrate the application of the DSVSI np chart. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the population varianceS2yof the study variableyusing auxiliary information in sample surveys. We have suggested a class of estimators of the population varianceS2yof the study variableywhen the population varianceS2xof the auxiliary variablexis known. Asymptotic expressions of bias and mean squared error (MSE) of the proposed class of estimators have been obtained. Asymptotic optimum estimators in the proposed class of estimators have also been identified along with its MSE formula. A comparison has been provided. We have further provided the double sampling version of the proposed class of estimators. The properties of the double sampling version have been provided under large sample approximation. In addition, we support the present study with aid of a numerical illustration. [ABSTRACT FROM AUTHOR]

Although the classical Shewhart np control chart has been widely used to detect an out-of-control status of manufacturing process, it is static and there is lack of responsiveness to slight process changes. In this paper, an adaptive np control chart with a joint sampling strategy combining double sampling (DS) and variable sampling interval (VSI) is developed. The multiple dependent state sampling scheme is adopted to further improve the performance of the control chart. An economical design model to minimize the general cost of using the proposed chart is established and solved by a genetic algorithm. The numerical results show that comparing to traditional static np control chart, the proposed np chart yields better performance in terms of shorter time to signal an out-of-control process and less expected cost per unit of time. Comparisons are made to show the capability of the proposed chart in yielding average reductions of 5.01% and 8.89%, in the cost of the proposed model compared to situations in which either the DSVSI np chart or the traditional np chart is used. [ABSTRACT FROM AUTHOR]

In this paper a class of improved estimators has been proposed for estimating population mean in Two Phase (Double) Sampling when information of auxiliary variable is available. Under Simple Random Sampling (SRSWOR), expressions of Mean Square Error and bias have been derived to make comparison of suggested class with wide range of other estimators. Empirical study has also been given using five different natural populations. Empirical study confirmed that the suggested class of improved estimators is more efficient under relative efficiency criterion. We have also developed some ranges of constant where we get minimum mean square error. These constant values are useful for surveyor. [ABSTRACT FROM AUTHOR]

Semi-competing risks data arise when two types of events, non-terminal and terminal, may be observed. When the terminal event occurs first, it censors the non-terminal event. Otherwise the terminal event is observable after the occurrence of the non-terminal event. In practice, it can be hard to ascertain all terminal event information after the non-terminal event. Yu and Yiannoutsos [(2015), ‘Marginal and Conditional Distribution Estimation from Double-Sampled Semi-Competing Risks Data’,Scandinavian Journal of Statistics, 42, 87–103] considered a setting when the terminal event is ascertained via double sampling from only a subset of patients who experienced the non-terminal event. They discussed estimation for marginal and conditional distributions under this double sampled semi-competing risk data framework. We propose a more efficient estimation method in the same setting by fully utilising the non-terminal event information. The efficiency gain can be substantial as observed in our simulation study. [ABSTRACT FROM AUTHOR]

A two-stage randomized response model is extended to stratified random sampling in order to find out more efficient estimators of proportions built from sensitive questions, which respondents may not answer truthfully, in a population divided into homogeneous subgroups. In each subgroup, the respondents who have not answered the sensitive question in the first stage are requested in the second stage to either answer the sensitive question (second attempt then) or to draw a card indicating “yes” or “no”. In the latter case, they are required to report the outcome. Such extension provides a more efficient estimator of the proportion of the population having a given sensitive attribute than its counterpart in simple random sampling. The extended two-stage randomized response model is more efficient than the stratified randomized response model, where respondents must answer the sensitive question either in the first or in the second stage. Moreover, it increases the respondents’ cooperation. When strata weights are unknown, they are estimated by the double sampling method. [ABSTRACT FROM AUTHOR]

In this article, ratio estimators for the population mean are suggested using the robust regression under the double sampling scheme. The mean squared error (MSE) expressions are obtained for the first degree of approximation. Theoretical comparisons show that the proposed estimators having the robust regression estimates are more efficient than the estimators using the least square method under the certain conditions. Theoretical findings are supported with the aid of a real life dataset in application and a simulation study is also conducted to evaluate the performance of the proposed estimators. [ABSTRACT FROM AUTHOR]

Nonresponse is very common in epidemiologic surveys and clinical trials. Common methods for dealing with missing data (e.g., complete-case analysis, ignorable-likelihood methods, and nonignorable modeling methods) rely on untestable assumptions. Nonresponse two-phase sampling (NTS), which takes a random sample of initial nonrespondents for follow-up data collection, provides a means to reduce nonresponse bias. However, traditional weighting methods to analyze data from NTS do not make full use of auxiliary variables. This article proposes a method called nonrespondent subsample multiple imputation (NSMI), where multiple imputation () is performed within the subsample of nonrespondents in Phase I using additional data collected in Phase II. The properties of the proposed methods by simulation are illustrated and the methods applied to a quality of life study. The simulation study shows that the gains from using the NTS scheme can be substantial, even if NTS sampling only collects data from a small proportion of the initial nonrespondents. [ABSTRACT FROM AUTHOR]

The present investigation deals with the problem of estimation of population mean in two-phase sampling. In the presence of two auxiliary variables, some classes of estimators have been proposed through predictive approach. Properties of the proposed classes of estimators have been studied, and the unbiased versions of these estimators along with their approximate variance expressions are obtained under simple random sampling without replacement scheme. The respective optimum strategies of the proposed estimators are discussed, and their empirical and graphical comparisons with some contemporary estimators of population mean have been made. Suitable recommendations to the survey practitioner are given. [ABSTRACT FROM PUBLISHER]

We suggested the class of estimators of the population mean with its bias and mean square error. It has been shown that the suggested class is more efficient than the usual unbiased, ratio, product and regression estimators and estimators due to Bahl and Tuteja (1991), Singh et al. (2009), and Upadhyaya et al. (2011). In addition an empirical study also carried out to and founded that the members of suggested family also have improvement over Grover and Kaur (2011) and Shabbir and Gupta (2011) classes. Two-phase (double) sampling version of the proposed class was also given. [ABSTRACT FROM PUBLISHER]

Regression is a criterion for estimating the relationships among variables. Though many authors have derived regression estimator on double sampling by making use of auxiliary variables without stratification and obtained their variances. Hence, there is need to determine a modified regression estimator on double sampling by making use of auxiliary variables with stratification for estimating the populat ion estimates. This study proposed an estimator for estimating the populat ion estimates of double sampling involving stratification. The data were collected on cost of House Rent, Transportation, Feeding, Fueling of Car, PHCN Bill, Toiletr ies, Fueling of Generator, Recharge Card and total expenditure of some randomly selected staff members of Federal University of Agriculture, Abeokuta, Ogun State, Nigeria. A random sample of s ize 138 was randomly selected from 256 respondents and sub sample of 81 was taken from the first sample. Auxiliary variables in the first sample and sub-sample (cost of House Rent, Transportation, Feeding, Fueling of Car; PHCN Bill, Toiletr ies, Fueling of Generator and Recharge Card) were stratified into two vector groups using level of expenditure and income as stratifying factors respectively. The mean and the minimum variance of the double sampling for the existing estimator are 92,634 and 2,672,766,891 respectively, while the mean and variance of the proposed estimator are 196,330.1 and 3,114,477 respectively. The minimum variance of the proposed estimator is smaller than the minimum variance of the existing estimator; hence, the proposed estimator is an efficient estimator. [ABSTRACT FROM AUTHOR]

Population with large number of elements remains unchanged in several occasions but the value of units changes. There are existing works on changes in population estimates from one occasion to another using double sampling approach. There is dearth of information on the estimation of population estimate using double sampling approach with stratification. This study proposed an estimator to determine the population estimates of two occasions involving stratification using successive sampling with two different secondary data. A random sample of size 120 was selected from 260 prices of milled rice and 40 from 97 immunized children on the first occasion. On the second occasion, the samples were stratified and subsamples of 60 from the price of milled rice and 20 from the immunized children were selected from each stratum which was then supplemented with a post stratified fresh sample of 60 and 40 for both data respectively. The variance of double sampling estimator and minimum variance for the successive sampling for the existing and proposed estimators were obtained. The variance of the existing estimator for double sampling for the price of milled rice and the number of immunized children were 40,914.20 and 1,870,842.00 respectively, while the variance of the proposed estimator were 26,198.11 and 799,064.40 respectively. The minimum variances for the existing estimator for the price of milled rice and the number of immunized children were 25,572.38 and 677,564.40 respectively, while the minimum variances for the proposed estimator were 21,513.36 and 577,275.30 respectively. The variance of the proposed estimator is less than that of the existing estimator, hence more efficient. [ABSTRACT FROM AUTHOR]

Randomized response technique is an effective research method that is used to estimate the proportion of a population that possesses a sensitive characteristic, such as tax evasion, abortion, or drug abuse. In this paper, two recently developed randomized response models using two decks of cards are extended to the case of stratified random sampling. Under proportional allocation or optimal allocation for fixed sample sizes, it is shown that the proposed stratified estimators are always more efficient than their counterparts in simple random sampling. Comparing the two proposed models, it is found that one of the models can be adjusted to be more efficient than the other, and that this model can increase the respondents' cooperation. Hence this model is referred to as the improved model. For unknown strata sizes, the double sampling method for stratification is applied. [ABSTRACT FROM AUTHOR]

An index-calibration experiment involves rigorous estimation of animal abundance at a small scale to calibrate a less rigorously derived index of abundance. The efficacy of such index-calibration experiments has been a matter of much controversy. In this study, we develop theoretical models and test them with empirical data on large-scale index-calibration experiments on tigers Panthera tigris to advance our understanding of this controversy., We propose two models that describe the sampling processes involved in typical index-calibration experiments. Using analytical derivations and some simulations, we evaluate the relative roles of these sampling parameters on the [ABSTRACT FROM AUTHOR]

In this article, we construct two likelihood-based confidence intervals (CIs) for a binomial proportion parameter using a double-sampling scheme with misclassified binary data. We utilize an easy-to-implement closed-form algorithm to obtain maximum likelihood estimators of the model parameters by maximizing the full-likelihood function. The two CIs are a naïve Wald interval and a modified Wald interval. Using simulations, we assess and compare the coverage probabilities and average widths of our two CIs. Finally, we conclude that the modified Wald interval, unlike the naïve Wald interval, produces close-to-nominal CIs under various simulations and, thus, is preferred in practice. Utilizing the expressions derived, we also illustrate our two CIs for a binomial proportion parameter using real-data example. [ABSTRACT FROM AUTHOR]

In this article, a chain ratio-product type exponential estimator is proposed for estimating finite population mean in stratified random sampling with two auxiliary variables under double sampling design. Theoretical and empirical results show that the proposed estimator is more efficient than the existing estimators, i.e., usual stratified random sample mean estimator, Chand (1975) chain ratio estimator, Choudhary and Singh (2012) estimator, chain ratio-product-type estimator, Sahoo et al. (1993) difference type estimator, and Kiregyera (1984) regression-type estimator. Two data sets are used to illustrate the performances of different estimators. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed an estimator for finite population mean using auxiliary information in form of attributes under double sampling. We have also derived the expression of mean squared error of the proposed estimator. The proposed estimator is compared with the simple mean per unit, usual double sampling ratio and product estimators. An empirical study is carried out to judge the merit of the proposed estimator. [ABSTRACT FROM AUTHOR]

In stratified sampling when strata weights are unknown a double sampling technique may be used to estimate them. A large simple random sample from the unstratified population is drawn and units falling in each stratum are recorded. A stratified random sample is then selected and simple random subsamples are obtained out of the previously selected units of the strata. This procedure is called double sampling for stratification. If the problem of non-response is there, then subsamples are divided into classes of respondents and non-respondents. A second subsample is then obtained out of the non-respondents and an attempt is made to obtain the information by increasing efforts, persuasion and call backs. In this paper, the problem of obtaining a compromise allocation in multivariate stratified random sampling is discussed when strata weights are unknown and non-response is present. The problem turns out to be a multiobjective non-linear integer programming problem. An approximation of the problem to an integer linear programming problem by linearizing the non-linear objective functions at their individual optima is worked out. Chebyshev's goal programming technique is then used to solve the approximated problem. A numerical example is also presented to exhibit the practical application of the developed procedure. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose a class of chain ratio-type exponential estimators for population mean in double sampling using two auxiliary variates. The properties of the proposed class of estimators are studied under large sample approximation. It has been shown that the proposed class of estimators is more efficient than the ratio estimator in double sampling , Chand’s (1975) chain-type ratio estimator and Singh and Vishwakarma (2007) exponential ratio estimator. An empirical study is carried out in support of the present study. [Copyright &y& Elsevier]

We consider data with a nominal grouping variable and a binary response variable. The grouping variable is measured without error, but the response variable is measured using a fallible device subject to misclassification. To achieve model identifiability, we use the double-sampling scheme which requires obtaining a subsample of the original data or another independent sample. This sample is then classified by both the fallible device and another infallible device regarding the response variable. We propose two Wald tests for testing the association between the two variables and illustrate the test using traffic data. The Type-I error rate and power of the tests are examined using simulations and a modified Wald test is recommended. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a chain ratio–product type estimators has been developed for estimating population mean of the study variable using two auxiliary variables under double sampling scheme, when the information on another additional auxiliary character is available along with the main auxiliary character. The optimum property of the proposed strategy has been identified in two different cases with their variances. Theoretical and empirical studies have been done to demonstrate the efficiency of the proposed estimator over other estimators, which utilized the information on two auxiliary characteristics. [Copyright &y& Elsevier]

Investigating the prevalence of a disease is an important topic in medical studies. Such investigations are usually based on the classification results of a group of subjects according to whether they have the disease. To classify subjects, screening tests that are inexpensive and nonintrusive to the test subjects are frequently used to produce results in a timely manner. However, such screening tests may suffer from high levels of misclassification. Although it is often possible to design a gold-standard test or device that is not subject to misclassification, such devices are usually costly and time-consuming, and in some cases intrusive to the test subjects. As a compromise between these two approaches, it is possible to use data that are obtained by the method of double-sampling. In this article, we derive and investigate four test statistics for testing a hypothesis on disease prevalence with double-sampling data. The test statistics are implemented through both the asymptotic method suitable for large samples and approximate unconditional method suitable for small samples. Our simulation results show that the approximate unconditional method usually produces a more satisfactory empirical type I error rate and power than its asymptotic counterpart, especially for small to moderate sample sizes. The results also suggest that the score test and the Wald test based on an estimate of variance with parameters estimated under the null hypothesis outperform the others. An real example is used to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

Combining information from two or more independent surveys is a problem frequently encountered in survey sampling. We consider the case of two independent surveys, where a large sample from survey 1 collects only auxiliary information and a much smaller sample from survey 2 provides information on both the variables of interest and the auxiliary variables. We propose a model-assisted projection method of estimation based on a working model, but the reference distribution is design-based. We generate synthetic or proxy values of a variable of interest by first fitting the working model, relating the variable of interest to the auxiliary variables, to the data from survey 2 and then predicting the variable of interest associated with the auxiliary variables observed in survey 1. The projection estimator of a total is simply obtained from the survey 1 weights and associated synthetic values. We identify the conditions for the projection estimator to be asymptotically unbiased. Domain estimation using the projection method is also considered. Replication variance estimators are obtained by augmenting the synthetic data file for survey 1 with additional synthetic columns associated with the columns of replicate weights. Results from a simulation study are presented. [ABSTRACT FROM PUBLISHER]

We consider two-sample binary data consisting of two independent studies. One study is the main study where individuals are classified using a fallible classifier prone to error; the other study is a validation study where individuals are classified using both the fallible classifier and a gold standard which does not misclassify individuals. For such data, we propose a Bayesian model for making statistical inference for all model parameters and particularly the risk difference. We derive a closed-form algorithm for sampling from the posterior distribution. We then illustrate our algorithm using a real data example and conduct Monte Carlo simulation studies to show that our algorithm performs very well under various scenarios. [ABSTRACT FROM PUBLISHER]

Using double sampling technique, a generalized difference-cum-ratio type estimator has been proposed for estimating the population mean. The expressions for bias and mean square error of proposed estimator have been obtained. The conditions under which proposed estimator is better than the regression estimator and mean per unit estimator have also been obtained. The gain in efficiency over the existing ones have been illustrated numerically as well as graphically. Simulation study has also been done to support the results by generating data. [ABSTRACT FROM AUTHOR]

In this article, we propose a double-sampling (DS) np control chart. We assume that the time interval between samples is fixed. The choice of the design parameters of the proposed chart and also comparisons between charts are based on statistical properties, such as the average number of samples until a signal. The optimal design parameters of the proposed control chart are obtained. During the optimization procedure, constraints are imposed on the in-control average sample size and on the in-control average run length. In this way, required statistical properties can be assured. Varying some input parameters, the proposed DS np chart is compared with the single-sampling np chart, variable sample size np chart, CUSUM np and EWMA np charts. The comparisons are carried out considering the optimal design for each chart. For the ranges of parameters considered, the DS scheme is the fastest one for the detection of increases of 100% or more in the fraction non-conforming and, moreover, the DS np chart is easy to operate. [ABSTRACT FROM AUTHOR]

Consider independent observations X1, X2,... having a common normal probability density function [image omitted] with - ∞ < x < ∞ and unknown variance σ2 ( > 0). We propose to estimate f(x;σ2) by a plug-in maximum likelihood (ML) two-stage estimator under the mean integrated squared error (MISE) loss function. Our goal is to make the associated risk not to exceed a preassigned positive number c, referred to as the risk-bound. Because no fixed-sample-size methodology would handle this estimation problem, we design an appropriate two-stage estimation methodology that is shown to satisfy the asymptotic (i) first-order efficiency property (Theorem 2.1), (ii) first-order risk-efficiency property (Theorem 2.2), as well as (iii) second-order efficiency property (Theorem 2.3). The performances of the proposed methodology for small, moderate, and large sample sizes are examined with the help of simulations. We have noticed some limited robustness of the proposed methodology under mixture-normal population densities, in which case the asymptotic second-order efficiency property (Theorem 3.1) is shown. Illustrations are included with real data and analysis. Overall, we feel that the proposed two-stage plug-in density estimation methodology performs remarkably well. [ABSTRACT FROM AUTHOR]

The article discusses a study that estimates the proportion of closely related species through a two-phase ratio estimator. It evaluates the performance of the estimator based on factors, including the relative costs of classification, the phenotypic classification accuracy for the target and nontarget species and magnitude of the target species proportion. Moreover, the method was assessed under which it could be recommended.

In this paper, we consider the double sampling (DS) $$\overline{X} $$ control chart for monitoring processes in which the observations can be represented as a first-order autoregressive moving average (ARMA(1, 1)) model. The properties of the DS $$\overline{X} $$ control chart with the sampling intervals driven by the rational subgroup concept are studied and compared with the Shewhart chart and the variable sample size (VSS) chart, both properly modified to account for the serial correlation. Numerical results show that the correlation within subgroups has a significant impact on the properties of the charts. For processes with low to moderate correlation levels, the DS $$\overline{X} $$ chart is substantially more efficient in detecting process mean shifts. [ABSTRACT FROM AUTHOR]

In this article, we consider the T2 chart with double sampling to control bivariate processes (BDS chart). During the first stage of the sampling, n1 items of the sample are inspected and two quality characteristics (x; y) are measured. If the Hotelling statistic [image omitted] for the mean vector of (x; y) is less than w, the sampling is interrupted. If the Hotelling statistic [image omitted] is greater than CL1, where CL1>w, the control chart signals an out-of-control condition. If [image omitted] , the sampling goes on to the second stage, where the remaining n2 items of the sample are inspected and [image omitted] for the mean vector of the whole sample is computed. During the second stage of the sampling, the control chart signals an out-of-control condition when the statistic [image omitted] is larger than CL2. A comparative study shows that the BDS chart detects process disturbances faster than the standard bivariate T2 chart and the adaptive bivariate T2 charts with variable sample size and/or variable sampling interval. [ABSTRACT FROM AUTHOR]

In this article, we introduce minimum divergence estimators of parameters of a binary response model when data are subject to false-positive misclassification and obtained using a double-sampling plan. Under this set up, the problem of goodness-of-fit is considered and divergence-based confidence intervals (CIs) for a population proportion parameter are derived. A simulation experiment is carried out to compare the coverage probabilities of the new CIs. An application to real data is also given. [ABSTRACT FROM AUTHOR]

In many situations the survey objectives are not limited to the estimation of simple population parameters like means or totals and include more complex characteristics. In particular it may be desirable to assess the population coefficient of variation. Various estimators of this parameter are discussed in the literature. Survey nonresponse may affect their properties by increasing the bias and/or variance. In this paper a nonresponse-corrected estimator of the population coefficient of variation based on double sampling for nonresponse is proposed. Its properties are studied for arbitrary sampling designs in both phases and the stochastic nonresponse model. An important special case of simple random sampling without replacement and deterministic nonresponse is also discussed. Then a more general situation of second phase nonresponse is considered and a comparison with the non-adjusted estimator is made. [ABSTRACT FROM AUTHOR]

Estimates of wildlife population sizes are frequently constructed by combining counts of observed animals from a stratified survey of aerial sampling units with an estimated probability of detecting animals. Unlike traditional stratified survey designs, stratum-specific estimates of population size will be correlated if a common detection model is used to adjust counts for undetected animals in all strata. We illustrate this concept in the context of aerial surveys, considering 2 cases: 1) a single-detection parameter is estimated under the assumption of constant detection probabilities, and 2) a logistic-regression model is used to estimate heterogeneous detection probabilities. Naïve estimates of variance formed by summing stratum-specific estimates of variance may result in significant bias, particularly if there are a large number of strata, if detection probabilities are small, or if estimates of detection probabilities are imprecise. [ABSTRACT FROM AUTHOR]

In this paper we have suggested a double sampling version of Sahai [10] estimator using simple random sampling without replacement (SRSWOR) at both phases and its properties are studied. The estimator is also compared with simple mean per unit for a given cost of the survey. An empirical study is carried out to demonstrate the performance of suggested estimator over usual estimators. [ABSTRACT FROM AUTHOR]

Sudden death testing can be utilized for deciding upon the lot acceptance of manufactured parts. Variables single, and double sampling plans are proposed for the lot acceptance of parts whose life follows a Weibull distribution with known shape parameter. The proposed plans are different from the existing ones in that the lot acceptance criteria do not depend on the estimated scale parameter. Design parameters of both sampling plans are determined by using the usual two-point approach. The number of groups is determined independently of the group size, and even independently of the shape parameter. Also, the double sampling plan can reduce the average number of groups required. The effects of mis-specification of the shape parameter on the probability of accepting the lots under the single sampling plan are analyzed & discussed. [ABSTRACT FROM AUTHOR]

two-phase sampling, the second-phase sample is often a stratified sample based on the information observed in the first-phase sample. For the total of a population characteristic, either the double-expansion estimator or the reweighted expansion estimator can be used. Given a consistent first-phase replication variance estimator, we propose a consistent variance estimator that is applicable to both the double-expansion estimator and the reweighted expansion estimator. The proposed method can be extended to multiphase sampling. [ABSTRACT FROM AUTHOR]