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Coefficient of variation (CV) control charts are widely used to monitor processes in which the mean of process characteristic varies and its standard deviation changes with the mean. However, such ...

espanolEn las zonas semiaridas de Argentina existe una variacion de los pastizales naturales asociados a diferentes unidades de paisaje. Los modelos digitales de terreno son herramientas que han permitido el estudio entre las variables fisicas del ambiente y la dinamica y patrones de los ecosistemas semiaridos. Por tal motivo el objetivo de nuestro trabajo fue determinar si existen diferencias en la productividad y composicion botanica del pastizal natural en las diferentes unidades geomorfologicas identificadas. La cuantificacion de la cantidad de biomasa se realizo por el metodo del doble muestreo y composicion botanica mediante el metodo de asignacion de rangos. Para la determinacion de las propiedades geomorfologicas se utilizo un modelo digital del terreno en donde se considero: pendiente, orientacion de laderas, altitud, indice de rugosidad del terreno e indice de humedad topografica. Se encontraron diferencias en cuanto a la productividad y composicion botanica del pastizal natural, influenciado de forma directa e indirecta por las caracteristicas geomorfologicas como la altura, pendiente y rugosidad. La historia de sobrepastoreo a lo largo de decadas en el piedemonte del area de estudio ha contribuido a la degradacion del pastizal mediante la perdida de especies perennes palatables y la instalacion de especies anuales y de baja calidad. Es necesario agregar estaciones de muestreo con escalas temporales mas amplias teniendo en cuenta la presion de pastoreo. EnglishIn the semi-arid areas of Argentina there is a variation of the natural grasslands associated with different landscape units. Digital terrain models are tools that have allowed the study of the physical variables of the environment and the dynamics and patterns of semi-arid ecosystems. For this reason, the objective of our work was to determine if there are differences in the productivity and botanical composition of the natural grassland in the different geomorphological units identified. The quantification of the amount of biomass was carried out by the method of double sampling and botanical composition by the method of assigning ranges. To determine the geomorphological properties, a digital model of the terrain was used, where the following were considered: slope, slope orientation, altitude, terrain roughness index and topographic humidity index. Differences were found in terms of the productivity and botanical composition of the natural grassland, influenced directly and indirectly by geomorphological characteristics such as height, slope and roughness. The decades-long history of overgrazing in the foothills of the study area has contributed to grassland degradation through the loss of palatable perennials and the installation of low-quality and annual species. It is necessary to add sampling stations with broader time scales taking into account grazing pressure.

In recent years, the impact of measurement errors on the performance of various control charts have been well evaluated. However, as far as we know, the multiple sampling control charts in the lite...

Skip-lot sampling plans have the provision of inspecting only a fraction of lots, which reduces time and cost of the inspection. Thus, the skip-lot sampling plan is more cost-effective. The intenti...

Adapted from the acceptance sampling field, the double sampling monitoring schemes implement a two-stage strategy to decide whether the process being monitored is in-control or out-of-control. That...

A modified form of the population mean estimator suggested by Anieting and Enang (2020) in stratified double sampling in the presence of nonresponse using a single auxiliary variable has been proposed. The Mean Squared Error (MSE) and the bias of the proposed estimator have been given using large sample approximation. The empirical study shows that the MSE of the suggested estimator is more efficient than all other existing estimators in the same scheme. Determination of the optimal values of the first and second phases samples has also been done

Recent studies have shown that an adaptive T 2 chart with double sampling and variable sampling interval D S V S I T 2 chart shows a good performance in detecting small to moderate shifts in the me...

This study investigates the process for estimating the mean number of individuals having rare sensitive attribute in stratified random sampling as well as in stratified random double sampling using...

In this paper, a ratio-product estimator for estimating the population mean of the study variable based on the population coefficient of skewness of the auxiliary variable is suggested in stratified double sampling. Asymptotic optimum estimator and its approximate bias and variance expressions are derived. Properties of the suggested estimator are studied with some known existing estimators identified as special members of this class of estimators. Analytical and numerical investigations showed that the suggested estimator is more efficient than the conventional regression estimator of mean in stratified double sampling and existing estimators of its class in stratified double sampling. Analysis and evaluation are presented.

This article examines the zero numerator problem. This problem occurs when the misclassification rates are so low that both forms of misclassification may not both be present in the validation data. For this problem, the article compares two Bayesian methods for analyzing misclassified binary data that has a validation sub-study and three forms of a non-informative prior distribution. It shows that the two methods give similar results. However, the posterior distributions were sensitive to the choice of the prior distribution when misclassification rates are low. The article further presents a simulation study that reveals that the bias is small regardless of the considered prior distribution. However, when the misclassification rates are low, the coverage of credible intervals is markedly better for a Jeffreys’ prior than for either a Haldane’s or a uniform prior. Finally, the article highlights the merit of quantifying expert opinion to form a subjective prior distribution when the posterior distribution is sensitive to the prior.

Double sampling for stratification (2SS) is a sampling design that is widely used for forest inventories. We present the mathematical derivation of two appropriate variance estimators for mean growth from repeated 2SS with updated stratification on each measurement occasion. Both estimators account for substratification based on the transition of sampling units among the strata due to the updated allocation. For the first estimator, sizes of the substrata were estimated from the second-phase sample (sample plots), whereas the respective sizes in the second variance estimator relied on the larger first-phase sample. The estimators were empirically compared with a modified version of Cochran’s well-known 2SS variance estimator that ignores substratification. This was done by performing bootstrap resampling on data from two German forest districts. The major findings were as follows: (i) accounting for substratification, as implemented in both new estimators, has substantial impact in terms of significantly smaller variance estimates and bias compared with the estimator without substratification, and (ii) the second estimator with substrata sizes being estimated from the first-phase sample shows a smaller bias than the first estimator.

Count data arising in various fields of applications are often under‐reported. Ignoring undercount naturally leads to biased estimators and inaccurate confidence intervals. In the presence of undercount, in this paper, we develop likelihood‐based methodologies for estimation of mean using validation data. The asymptotic distributions of the competing estimators of the mean are derived. The impact of ignoring undercount on the coverage and length of the confidence intervals is investigated using extensive numerical studies. Finally an analysis of heat mortality data is presented.

In this paper, we design an innovative and general class of modified two-stage sampling schemes to enhance double sampling and modified double sampling procedures. Under the squared error loss plus...

Kriging model has been widely used to approximate expensive black-box problems in many engineering design fields. How to choose an appropriate sampling strategy to produce new expensive updated points is crucial. For this purpose, an adaptive Kriging method with double sampling criteria (AKM-DSC) is proposed. During every iteration of it, maximum curvature criterion and maximum variance criterion based on Kriging model are respectively optimized by trust region (TR) strategy to produce two candidate points. And then, a new screening method is used to determine final expensive-evaluation points from the two candidates. The proposed method is compared with the two typical Kriging modeling methods. The comparison results of seventeen benchmark functions verify that the proposed method can generate higher accuracy Kriging model. Finally, a hydrogen preparation case illustrates the engineering application value of the AKM-DSC method.

In many applications, the quality of a process is characterized by the functional relationship between a response variable and one or more explanatory variables. This functional relationship is ref...

The present paper deals with the problem of estimating the product of two population means using the information of auxiliary character under double sampling the non-respondents. Two different cases are considered for suggesting the estimators to estimate the product of two population means. The bias and mean square errors are obtained up to the first order of approximation, and the conditions for obtaining the minimum mean square errors of suggested estimators are derived. Theoretical and empirical studies with real data sets published by the Government of India are carried out to demonstrate the efficiency of the proposed estimators over the relevant estimators used in practice.

Control charts based on inspection with gauges are an interesting alternative to monitor quantitative variables when measurement is complex or expensive. The main advantage over variable control ch...

In sequential tests, typically a (pairwise) multiple comparison procedure (MCP) is performed after an omnibus test (an overall equality test). In general, when an omnibus test (e.g., overall equality of multiple proportions test) is rejected, then we further conduct a (pairwise) multiple comparisons or MCPs to determine which (e.g., proportions) pairs the significant differences came from. In this article, via likelihood-based approaches, we acquire three confidence intervals (CIs) for comparing each pairwise proportion difference in the presence of over-reported binomial data. Our closed-form algorithm is easy to implement. As a result, for multiple-sample proportions differences, we can easily apply MCP adjustment methods (e.g., Bonferroni, &Scaron, id&aacute, k, and Dunn) to address the multiplicity issue, unlike previous literatures. We illustrate our procedures to a real data example.

In statistical process monitoring (SPM), most of the monitoring schemes are designed assuming that the process parameters for the underlying distribution are known (i.e., Case K). In a variety of c...

This paper presents an adaptive T2 control chart for monitoring shifts in the mean vector of a multivariate normally distributed process. This chart which combines the double sampling, variable sam...

Non-linear programming problems can be solved efficiently by geometric programming in which non-linear expressions can be expressed by an exponential or power functions. In this paper, Double Sampling Stratification (DSS) strategy is used in multivariate stratified population with unidentified strata weights, for optimum sampling design (OSD) in the presence of non-response to estimate the unidentified population means. The double sampling problem in the presence of non-response is formulated as Fuzzy Multiobjective Convex Programming Problem (FMOCPP). Then, we convert FMOCPP into Fuzzy Single Objective Convex Problem (FSOCP) by using membership function. Dual solution is obtained using LINGO Software by solving FSOCP. The optimum allocations of sample sizes of respondents and non- respondents for both the phases are obtained by applying dual solutions and primal-dual relationship theorem. The given technique is illustrated by projecting a numerical problem.

The primary aim of this research is to propose a revised triple sampling (TS) X¯ chart, where the derivations of new formulae for computing the average run length of the triple sampling (TS) X¯ chart correctly are provided. The secondary aim is to develop the revised TS X¯ chart with estimated process parameters. The revised TS X¯ charts are compared with the double sampling (DS) X¯, two stage adaptive sample size (AS2) X¯ and three stage adaptive sample size (AS3) X¯ charts when process parameters are known and estimated using the average run length (ARL), average number of observations to signal (ANOS), average of the average run lengths (AARL), standard deviation of the average run lengths (SDARL), average of the average number of observations to signal (AANOS) and standard deviation of the average number of observations to signal (SDANOS) criteria, where the revised TS X¯ charts are found to be superior. Additionally, a table giving the minimum number of Phase-I samples for estimating the process mean so that the revised TS X¯ chart with estimated process parameters has the desired in-control AARL and AANOS performances is provided.

In this study, we suggest a family of ratio estimators for the population mean parameter using various robust regression techniques. These robust regressions techniques are Huber MM, LTS, and LMS estimates. We evaluate the performance of estimators in terms of the mean square error (MSE), and we compare the efficiency of our proposed robust-regression-ratio-type estimators with existing estimators under the optimal conditions. These comparisons show that our robust ratio-type estimators give more efficient results than the existing estimators under double sampling. In addition, the simulation and the empirical studies based on a data set that includes unusual observations show that our proposed estimators have a lower MSE than the existing estimators.

This paper studies an attribute control chart for monitoring the number of nonconforming items using a triple sampling (TS-np) which has not yet been applied to attribute control charts. The chart design and procedure for the decision about the state of the process are given. Mathematical expressions for the average run length (ARL) for in-control and out-of-control processes and the average sample number (ASN) are given. A bi-objective genetic algorithm that seeks to minimize the ASN and the probability of type 2 error is implemented in order to optimize the design of the TS-np control chart. A comparison between TS-np, single sampling np (SS-np), double sampling np (DS-np), and multiple dependent state repetitive sampling (MDSRS) control charts is carried out in terms of the out-of-control average run length (ARL1). Tables of ARL1 values for TS-np are presented in comparison with MDSRS and DS-np for various scenarios. The operation of the proposed control chart is shown through simulated data. Finally, it is concluded that the proposed TS-np chart has a better performance in terms of ARL1 detecting small and moderate shifts in the process nonconforming rate in-control (p0) compared with MDSRS and DS-np.

In recent years, the suitable use of auxiliary information technique in control charts has shown an improved run length performance compared to control charts that do not have this feature. This article proposes a combined variable sampling interval (VSI) and double sampling (DS) chart using the auxiliary information (AI) technique (called VSIDS-AI chart, hereafter). The plotting-statistic of the VSIDS-AI chart requires information from both the study and auxiliary variables to efficiently detect process mean shifts. The charting statistics, optimal design and performance assessment of the VSIDS-AI chart are discussed. The steady-state average time to signal (ssATS) and steady-state expected average time to signal (ssEATS) are considered as the performance measures. The ssATS and ssEATS results of the VSIDS-AI chart are compared with those of the DS AI, variable sample size and sampling interval AI, exponentially weighted moving average AI (EWMA-AI) and run sum AI (RS-AI) charts. The results of comparison show that the VSIDS-AI chart outperforms the charts under comparison for all shift sizes, except the EWMA-AI and RS-AI charts for small shift sizes. An illustrative example is provided to demonstrate the implementation of the VSIDS-AI chart.

It is costly to collect the household- and individual-level data that underlies official estimates of poverty and health. For this reason, developing countries often do not have the budget to update their estimates of poverty and health regularly, even though these estimates are most needed there. One way to reduce the financial burden is to substitute some of the real data with predicted data. An approach referred to as double sampling collects the expensive outcome variable for a sub-sample only while collecting the covariates used for prediction for the full sample. The objective of this study is to determine if this would indeed allow for realizing meaningful reductions in financial costs while preserving statistical precision. The study does this using analytical calculations that allow for considering a wide range of parameter values that are plausible to real applications. The benefits of using double sampling are found to be modest. There are circumstances for which the gains can be more substantial, but the study conjectures that these denote the exceptions rather than the rule. The recommendation is to rely on real data whenever there is a need for new data, and use the prediction estimator to leverage existing data.

Volume growth is a key indicator in forest management and planning and, accordingly, an integral part of the estimation procedure of forest resources from sample based inventories. Growth estimation from successive double sampling for stratification (2SS) is somewhat challenging and has not been sufficiently addressed in the pertinent literature. Applying 2SS on successive occasions, with updated stratification on each occasion, may lead to fluctuation of sampling units among the strata and to a certain number of sample plots that have to be discarded or that have to be newly established on the second occasion, to obtain the required per-strata sampling proportions, which are stipulated in advance. After presenting a notation to implement growth estimation into 2SS standard formulas, the question of strata shifts and the occurrence of discarded and of new sample plots in the context of growth estimation is addressed. Although growth, unlike net change, can only be estimated from direct observations on remeasured sample plots, it was shown that ignoring discarded or new plots might lead to severely biased estimators. Modified estimators for mean growth and variances are provided and their application is illustrated using data from a repeated survey in a central German forest district.

Chain-ratio estimators are often used to improve the efficiency of the estimation of the population total or the mean using two auxiliary variables, available in two different phases. An improved c...

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 pap...

To estimate the disease prevalence, a gold standard test is the best choice for accurate estimation. The gold standard could be very expensive or time consuming for use in practice. For this reason...

This study presents a double sampling (DS) np chart when the process parameter is estimated. The DS np chart is evaluated in terms of the average of the average run length and the standard deviatio...

This paper develops a new double sampling (DS) monitoring scheme, namely, the side-sensitive DS X¯ chart, to monitor the process mean. The operational procedure is presented first followed by the e...

This paper develops a new synthetic double sampling (SDS) monitoring scheme based on the modified side-sensitive (MSS) design for jointly monitoring the process mean and variability. We first give the operation of the proposed scheme and secondly, the closed-form expression of the probability of declaring a specific sampling stage as “conforming” or “nonconforming”. Thirdly, we evaluate the performance of the newly proposed scheme in terms of the zero- and steady-state out-of-control average run-length, standard deviation of the run-length, average number of observations to signal, average extra quadratic loss, average ratio of the average run-length and performance comparison index. Finally, we compare the performance of the new monitoring scheme with some existing monitoring schemes. It is observed that the proposed scheme has attractive run-length properties and outperforms the existing joint X ¯ and S SDS monitoring scheme as well as all other competing schemes in many situations.

The quality loss suggested by Taguchi has been recognised as a new quality evaluation based on variable property instead of the quality evaluation based on traditional attribute property such as th...

A flexible scrambled response model using a randomization device for quantitative sensitive data is used to evaluate the protection of respondents’ privacy. A double-sampling regression-cum-exponen...

We construct a point and interval estimation using a Bayesian approach for the difference of two population proportion parameters based on two independent samples of binomial data subject to one type of misclassification. Specifically, we derive an easy-to-implement closed-form algorithm for drawing from the posterior distributions. For illustration, we applied our algorithm to a real data example. Finally, we conduct simulation studies to demonstrate the efficiency of our algorithm for Bayesian inference.

This manuscript presents the process for estimating the mean number of individuals having rare sensitive characteristic when population units are heterogeneous with and without prior information on the supplementary (unrelated rare non-sensitive) characteristic. The rare stigmatized parameter is estimated using an ameliorated two-stage randomized response model under stratified sampling and stratified double sampling schemes. The properties of the suggested estimators have been discussed under random, proportional and optimal allocations of sample from different strata. The proposed estimators perform better over some contemporary competent estimators for similar situations which have been shown through empirical studies.

Acceptance sampling plans are quality tools for the manufacturer and the customer. The ultimate result of reduction of nonconforming items will increase the profit of the manufacturer and enhance t...