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ABSTRACT Missing data arise in most applied settings and are ubiquitous in electronic health records (EHR). When data are missing not at random (MNAR) with respect to measured covariates, sensitivity analyses are often considered. These solutions, however, are often unsatisfying in that they are not guaranteed to yield actionable conclusions. Motivated by an EHR‐based study of long‐term outcomes following bariatric surgery, we consider the use of double sampling as a means to mitigate MNAR outcome data when the statistical goals are estimation and inference regarding causal effects. We describe assumptions that are sufficient for the identification of the joint distribution of confounders, treatment, and outcome under this design. Additionally, we derive efficient and robust estimators of the average causal treatment effect under a nonparametric model and under a model assuming outcomes were, in fact, initially missing at random (MAR). We compare these in simulations to an approach that adaptively estimates based on evidence of violation of the MAR assumption. Finally, we also show that the proposed double sampling design can be extended to handle arbitrary coarsening mechanisms, and derive nonparametric efficient estimators of any smooth full data functional. [ABSTRACT FROM AUTHOR]

Variable radius plot surveys can require updated estimates when the quality of previous estimates have declined over time. We developed a statistical estimator for this purpose that is the product of two estimators, one estimator is for the updated volume-to-basal-area ratio and the other estimator is for the updated basal area. Our estimator relies only on the surveyor identifying previously measured trees and does not require the identification of ingrowth, which simplifies its application. We also propose a variance estimator that relies on the bootstrap method. We investigate the point and variance estimators under repeated sampling of stem-mapped populations that include five measurements over a period of approximately 20 years. Based on 144 sampling scenarios, our estimator demonstrated relative biases ranging between −0.3% and 0.8%. The bootstrap variance estimator tended to be deflated, with relative absolute biases ranging between −8% and 4%. When revisiting one third of the initially installed plots, our estimator obtained relative root mean square errors within 1.6% of an independent resurvey effort that does not use the prior survey. This implies that our estimator can substantially reduce field work while producing surveys of approximately the same quality when updating prior surveys. [ABSTRACT FROM AUTHOR]

Three-level control charts have been developed for monitoring processes where the quality of products is characterized by classifying the product characteristic into three discrete levels. In this article, a combined double sampling and variable sampling interval (DSVSI) attribute control chart is developed for monitoring three-level products. This research is motivated by recent studies done on combining the DSVSI scheme with traditional attribute charts and their results recommend the application of the DSVSI attribute charts. The proposed DSVSI three-level chart is compared with the existing standard, adaptive, and EWMA three-level charts. The results approve the superiority of the proposed scheme and recommend it as an efficient three-level control chart in application. An example is also provided to illustrate the application of the proposed chart. [ABSTRACT FROM AUTHOR]

In order to improve upon the efficiency of an estimate in double sampling for estimating population mean of character under study using an auxiliary variable, a part of survey resources are used to collect the information on auxiliary variable. Some authors have suggested exponential-type estimators and some others advocated for log-type estimators. But combination of such is required for specific situation. This paper presents a class of logarithmic-cum-exponential ratio estimators in double sampling setup. The expressions for the mean squared error and bias of the proposed class of estimators are derived for two different cases(sub-sample and independent sample). Sometimes the persons involved in the sample survey have to undergo for risk on life. For example, data collection in naxalites area, working in intense forest, interview during spread of epidemic or data collection in politically disturbed region. Such risk may affect the accuracy, efficiency of estimation. A linear Risk function is used for the proposed class of estimators. Two cases of double sampling are compared in terms of relative efficiency in view to risk aspect. It is found that the proposed class of estimators has a lower mean squared error than the simple mean estimator, usual ratio, usual exponential, usual log estimators in the double sampling setup. In addition, these theoretical results are supported by a numerical example. Risk function based simulated study is performed for the support of findings of the content. Optimal sample sizes under risk are derived and compared under two cases. [ABSTRACT FROM AUTHOR]

Summary: Binary data subject to one type of misclassification exist in various fields. It is collected in a double‐sampling scheme that includes a gold standard test and a fallible test. The main parameter of interest for this type of data is the positive probability p$$ p $$ of the gold standard test. Existing intervals are unreliable because the given nominal level 1−α$$ 1-\alpha $$ is not achieved. In this paper, we construct an exact interval by inverting the E+M score tests and improve it by the general h$$ h $$‐function method. We find that the total length of the improved interval is shorter than the exact intervals that are also the improved intervals when we apply the h$$ h $$‐function to several existing approximate intervals, including the score and Bayesian intervals. Therefore, it is recommended for practice. We are also interested in two other parameters: p∗$$ {p}^{\ast } $$—the difference between the two positive rates for the fallible and gold standard tests—and ξ$$ \xi $$—the false positive rate for the fallible test. To the best of our knowledge, the research on these two parameters is limited. For p∗$$ {p}^{\ast } $$, we find that any interval for p$$ p $$ can be converted to an interval for p∗$$ {p}^{\ast } $$. So, the interval converted from the aforementioned recommended interval for p$$ p $$ is recommended for inferring p∗$$ {p}^{\ast } $$. For ξ$$ \xi $$, the improved interval by the h$$ h $$‐function method over the E+M score interval is derived. We use an example to illustrate how the intervals are computed and provide a real data analysis. [ABSTRACT FROM AUTHOR]

AbstractA process is in its stable state when it has sufficient precision and accuracy. The accuracy is expressed as the deviation of the process mean from its target while the low dispersion indicates the high process precision. Therefore, joint monitoring of multivariate process mean and variability ensures a high rate of producing items. This study proposes a multivariate generalized likelihood ratio scheme called DS-MGLR with two features: (1) considering the effect of imprecise observations, and (2) enhancing the chart sensitivity in reacting to process disturbances. Since the shift magnitude in the distribution parameters is not known in advance, the expected average run length is also calculated for both the MGLR and DS-MGLR charts. To reduce the impact of measurement errors, an improved version of the DS-MGLR chart based on multiple measurement approach is developed. Three comparative studies are presented to indicate the importance of the properties of the developed charts. The first study confirms the efficiency of the double sampling strategy, while the second study indicates the error effect. The third one shows the effectiveness of the multiple measurement approach in compensating for the error contamination. Finally, the applicability of the proposed chart is highlighted using a real data example. [ABSTRACT FROM AUTHOR]

AbstractToday, manufacturers face two challenges: the financial limitation of sampling and the large number of study quality characteristics. In such situations, the problem dimension is usually larger than the sample size, which is known in the literature of statistical quality control as a high dimensional process. The use of conventional multivariate charts to monitor the covariance matrix of such processes is not possible for two reasons: (1) the non-reversibility of the sample covariance matrix, (2) the possibility of the occurrence of sparse shifts in which only a few elements of the covariance matrix are affected. This paper focuses on Phase II monitoring of the covariance matrix of high-dimensional processes based on integrating the ridge penalized likelihood ratio (RPLR) statistic and the combined DS-VSS sampling method, in which DS and VSS features are used concurrently. The performance of the proposed DS-VSS RPLR control chart is compared with the RPLR one in terms of three metrics of the average run length, standard deviation of the run length, and expected value of sample size under three types of joint diagonal/off-diagonal, diagonal and off-diagonal shift patterns. The results show that the DS-VSS scheme is more sensitive than the RPLR in detecting both sparse and non- sparse changes. [ABSTRACT FROM AUTHOR]

The recently proposed semantic-aligned matching detection transformer (SAM–DETR model) accelerates the convergence of the detection transformer (DETR) by mapping object queries into an identical embedding space as the encoder's output feature map. However, SAM–DETR model has the problem of low detection accuracy compared to other DETR variants. We observe that the lower detection accuracy of SAM–DETR model is caused by the insufficient number of sample points and the inaccurate localization of the sample points during re-sampling, which blurs the generated attention map. This paper proposes an object detector based on a feature correction and double sampling DETR (FCDS-DETR) to solve this problem. FCDS-DETR takes SAM–DETR model as a baseline and builds on it by adding a feature correction module and a double sampling mechanism to achieve further improvement in detection accuracy with a limited number of additional parameters without sacrificing convergence speed. Firstly, FCDS-DETR improves the sampling point localization accuracy by adding a feature correction module to model the inter-channel dependence of the feature maps to be sampled. Secondly, the number of sampled points is increased by the double sampling mechanism, and attention fusion is used to fuse the attention weight maps corresponding to the two sets of sampled points to improve the recognizability of the attention weight maps. The experimental results show that the average precision is improved by +0.7 on the COCO dataset compared with the SAM–DETR model, and the number of parameters is increased by only 10.34 % , which improves the detection performance of the model very well. [ABSTRACT FROM AUTHOR]

Of interest in this paper is the development of a model that uses fixed, then inverse sampling of binary data that is subject to false-positive misclassification in an effort to estimate a proportion. From this model, both the proportion of success and false-positive misclassification rate may be estimated. Also, three first-order likelihood-based confidence intervals for the proportion of success are mathematically derived and studied via a Monte Carlo simulation. The simulation results indicate that the likelihood ratio interval is generally preferable over the Wald and score interval. Lastly, the model is applied to two different real-world medical data sets. [ABSTRACT FROM AUTHOR]

Transformation techniques have been used to increase the efficiency of estimators in sample surveys. In this paper, some classes of population mean estimators using transformation on an auxiliary variable and on both the auxiliary and study variables have been proposed under double sampling. The proposed estimators’ biases and mean square errors are approximated up to the first order. A simulation study and application to a rubber production dataset have been used to illustrate the proposed estimators’ performance. The results show that they perform much better than other existing estimators under given conditions. [ABSTRACT FROM AUTHOR]

Abstract Double sampling is usually applied to collecting disease information of individuals for situations, in which a gold standard is available for validating a subset of the sample that has been classified by a fallible classifier. Inference procedures are developed for this type of double-sampling data. However, it could happen in practice that such gold standard does not exist. When both false-positive and false-negative diagnoses exist for two classifiers, we propose several likelihood-based and Bayesian methods for estimating the disease prevalence, diagnostic-test sensitivity and specificity under two double-sampling models. A variety of methods including Wald, log-transformation, logit-transformation, inverse-hyperbolic-tangent-transformation, Bootstrap-resampling, Bayesian are proposed to construct confidence intervals (CIs) for these parameters. Moreover, the method of variance estimates recovery (MOVER) is proposed to find the CIs for their difference. The performances of the proposed CIs are evaluated and compared with respect to the empirical coverage probability, coverage width and ratio of mesial non-coverage to non-coverage probability. Empirical results show that all CIs except for Bayesian credible interval with Jeffery non-informative prior and Bootstrap CI based on normality assumption generally produce satisfactory results, and hence be recommended for practical applications. The data from a study of dementia disorders is used to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

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 is, a master sample is split into two separate subgroup samples, with the first subgroup sample used in the first stage and, depending on which type of double sampling method is used, either only the second or the combined first and second, subgroup sample(s) are used in the second stage. This strategy has been proven to effectively decrease the sampling effort and, at the same time, to decrease the time to detect potential out-of-control situations. For these reasons, it has received some attention in the statistical process monitoring (SPM) literature and, in this review paper, all 92 existing publications on the basic double sampling monitoring schemes and other different schemes that are integrated with the basic double sampling schemes are reviewed. The double sampling schemes are categorized and summarized so that any research gaps in the SPM literature can easily be identified. Finally, concluding remarks and some directions for future research ideas are given. [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]

Abstract. The concept of monitoring the coefficient of variation has gained significant interest in quality control, particularly in situations where the mean and standard deviation of a process are not constant. This study modified the procedure of the previous double sampling chart for monitoring the coefficient of variation, developed by Ng et al. in 2018. Instead of using only information from the second sample, here, information from both samples is used. The probability properties of the out-of-control signal and run length of this chart are presented. To evaluate the chart's performance, the optimal design and a comparison with the previous double sampling control chart using average run-length criteria are described. It was found that the modified double sampling chart has better performance and is more efficient compared to the previous chart, especially when the total sample size is smaller. As a study case, the application of this chart is illustrated using real data from a molding process. This confirmed that the modified double sampling chart improved performance in detecting out-of-control signals. Thus, the modified chart is recommended to be applied in industry. [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 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. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive control (MPC) with a double-sampling technique to spread the current spectrum. Finite control set MPC has the characteristic of a switching frequency that fluctuates. Thus, the components of the switching frequency and harmonics are reduced in the current spectrum. When MPC is utilized to control current, virtual vectors are added to ensure a low current total harmonic distortion (THD). However, increasing the voltage vector reduces the switching frequency variation, which leads to degradation of the spread-spectrum performance. The proposed method allows MPC to have an additional feature to change the harmonic components while reducing the current THD. To verify the proposed scheme, a comparison of its performance according to the sampling techniques and number of the voltage vectors has been conducted. The simulation and experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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 linear cost of sampling, we revisit the classic problem of minimum risk point estimation (MRPE) for an unknown normal mean μ ( ∈ R ) when the population variance σ 2 ( ∈ R + ) also remains unknown. With stopping variables constructed based on an arbitrary general estimator Wm for σ, which satisfies a set of certain conditions, our procedures are proved to enjoy asymptotic first- and second-order efficiency as well as asymptotic first-order risk efficiency. For illustrative purposes, we further investigate specific modified two-stage MRPE procedures, where we substitute appropriate multiples of sample standard deviation, Gini's mean difference (GMD), and mean absolute deviation (MAD) in the place of Wm, respectively. Extensive simulation studies are utilized to validate our theoretical findings. A real-life data set of weight change from female anorexic patients is then analyzed to demonstrate the practical applicability of these modified two-stage MRPE procedures. Comparing them in the case where there exist suspect outliers in the pilot sample, we are empirically confident that the GMD- and MAD-based procedures appear more robust than the sample-standard-deviation-based procedures. [ABSTRACT FROM AUTHOR]

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 ( A R L 1 ). Tables of A R L 1 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 A R L 1 detecting small and moderate shifts in the process nonconforming rate in-control ( p 0 ) compared with MDSRS and DS-np. [ABSTRACT FROM AUTHOR]

Poisson regression is used to deal with count data. The Poisson regression ratio estimator of the population mean is extended from single to double sampling. This is made possible by the provision of the population mean of an auxiliary variable. The mean square errors of the proposed estimators are expressed up to the first order. Theoretical and numerical results demonstrate that the proposed double-sampling Poisson-regression ratio estimator has a lower mean square error than the double-ratio and the single-sampling estimator. For Covid-19, the minimum mean square errors yielded by the proposed estimator of the total number of cases are 0.095 cases per day and 67.8 cases, compared with 0.112 cases per day and 84.8 cases with the double-ratio estimator. [ABSTRACT FROM AUTHOR]

Survey practitioners commonly encounter various types of nonresponse and strive to implement methods that mitigate any resulting bias when reporting results. In national forest inventories (NFI), complete or partial nonresponse usually results from hazardous conditions or lack of plot access permission. While many factors may be related to nonresponse, the two primary factors in the NFI of the USA are public/private land ownership and office/field plot status. To ameliorate potential nonresponse bias, these factors should be accounted for in the estimation process. An estimation method is presented where response homogeneity groups (RHGs) account for differential nonresponse rates between forest/nonforest plots. In a post-stratified estimation context, ratio-to-size estimators are used in RHGs within post-strata to avoid potential bias in variance estimates arising from partial plot nonresponse. Combining RHGs within post-strata requires a complex variance estimator that includes four sources of uncertainty. Testing of the estimation method on a synthetic population showed the approach is essentially unbiased. Application to NFI data from 10 states in the USA consistently showed the RHG method produced state-level estimates of forestland area that were 0.1%–3.6% larger than the current post-stratified estimation procedure. It is suggested that these differences are indicative of the nonresponse bias present when plots having differential nonresponse rates are not accounted for. [ABSTRACT FROM AUTHOR]

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 sample size and variable sampling interval features, called the DSVSSI T2 chart and uses three different sample sizes, two different sampling intervals, two warning limits and two different control limits. A Markov chain approach is employed to compute the statistical performance measures. The proposed chart is compared with the existing standard T2 and other adaptive multivariate T2 charts. In all cases, the DSVSSI T2 chart is quicker at detecting small to moderate shifts in the mean vector. An example is provided to illustrate the construction and implementation of the DSVSSI T2 chart. Finally, some concluding remarks are given. [ABSTRACT FROM AUTHOR]

In most applications, parametric monitoring schemes are used to monitor the majority of industrial and nonindustrial processes in order to improve the quality of the outputs or services. However, parametric monitoring schemes are known to underperform when the normality assumption is not met or when there is not enough information about the symmetry or asymmetry nature of the process underlying distribution. Hence, in this paper, a new nonparametric Phase II Shewhart-type double-sampling (DS) monitoring scheme based on the precedence statistic is proposed in order to efficiently monitor quality processes when the underlying process distribution departs from normality. The performance is investigated using the average run length (ARL), standard deviation of the run length (SDRL), expected ARL (EARL) and expected average number of observations to signal (EANOS), and the average sample sizes (ASS) metrics. The latter metrics are computed using Monte Carlo simulation and exact formulae. In general, it is shown that the new DS precedence scheme outperforms the existing basic Shewhart precedence scheme with and without supplementary runs rules in many situations. A real-life illustrative example based on a filling process of milk bottles is provided to demonstrate the application and implementation of the new DS precedence monitoring scheme. [ABSTRACT FROM AUTHOR]

In this study, we propose a design for a control chart for the normal distribution using a modified double sampling scheme. The probability of in control, out of control, the average run length (ARL), and the standard deviation of run length (SDRL) of the proposed control chart is derived. The proposed control chart using the modified double sampling scheme is found to be more efficient in detecting small shifts of a process in comparison with existing, single, and double sampling control charts. The efficiency of the proposed control chart is investigated in terms of ARL and SDRL via simulation and application to a real data set. [ABSTRACT FROM AUTHOR]

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 deviation of the average run length. The results show that a large number of Phase-I samples is required for designing the DS np chart. Since it is unrealistic in practice to obtain a large amount of Phase-I data, a combination of new design parameters for the DS np chart with estimated process parameter is computed by taking the number of Phase-I samples into consideration. [ABSTRACT FROM AUTHOR]

We derive and examine the interval width and coverage properties of an integrated‐likelihood‐ratio confidence interval for the binomial parameter p using a double‐sampling scheme. The data consist of a relatively large fallible sample containing underreported data and a relatively small infallible subsample. Via Monte Carlo simulations, we determine that the new integrated‐likelihood‐ratio interval estimator displays slightly conservative to moderately conservative coverage properties for small to medium sample sizes and can have shorter average‐interval width than two previously proposed confidence intervals when p < 0.10 or p > 0.90. We also apply the integrated‐likelihood‐ratio confidence interval to a real‐data set and determine that the integrated‐likelihood‐ratio interval has superior performance when contrasted to two properties of two competing confidence intervals. [ABSTRACT FROM AUTHOR]

Double sampling technique and control charts are used for predicting about unknown parameters of the big population and developing algorithms for imposing control over growth factor. This sampling procedure has two approaches like sub-sample and independent sample. Aim is to estimate mean filesize by both and to find out which approach is better in big data setup. Comparative mathematical tools used herein are mean squared error, confidence interval, relative confidence interval length measure and control charts of digital file-size for monitoring. Estimation strategies are proposed and confidence intervals are computed over multiple points of time. At each time, it was found that confidence intervals are catching the true values. First kind of approach (as case I) of double sampling found better than the second. A new simulation strategy is proposed who is observed efficient for comparison purpose. Single-valued simulated confidence intervals are obtained using the new simulation strategy and found covering the truth in its range. As an application of outcomes, control charts are developed to monitor the parametric growth over long duration. Upper and Lower control limits are drawn for business managers to keep a watch on digital file-size estimates whether their growth under control? Outcomes may be extended for reliability evaluation under discrete time domain. The content herein is a piece of thought, idea and analysis developed by deriving motivation from past references to handle big data using double sampling. Findings of the study can be used for developing software based monitoring system using process control charts for managers. [ABSTRACT FROM AUTHOR]

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 referred to as a profile. We extend two existing methods for monitoring the shift of slope of linear profiles. We consider the single sampling (SS) and the double sampling (DS) scheme for the economic-statistical design (ESD) model of profile monitoring in Phase II. The results of a numerical example indicate the DS β ^ 1 control chart has better performance than the DS T 2 control chart when ρ β ^ 0 , β ^ 1 is low. Comparison of the ESD of DS β 1 ^ and the SD of DS β 1 ^ control charts show that an ESD has an improvement over the pure SD. Also, we compare the ESD of the DS β 1 ^ with the ESD of the SS β 1 ^ control chart. The results show that the DS β 1 ^ control charts are more efficient in terms of minimizing the E (L). [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]

This paper presents a 2-2 multi-stage-noise-shaping (MASH) ΔΣ analog-to-digital converter (ADC), in which an integrator with dual double sampling (DDS) technique and stability compensation is proposed to save the area of capacitors without increasing the thermal noise. In addition, a fast-charge correlated-level-shifting (CLS) input buffer is also proposed to improve the linearity with acceptable power consumption. The buffer and the ADC are fabricated in a 0.18-μm CMOS process with an active area of 2.41 mm2, with 3∼5V supply voltage for the input buffer and 1.8-V power supply for the ADC, the overall ADC chip achieves a signal-to-noise-and-distortion ratio (SNDR) of 103.3 dB, a spurious-free dynamic range (SFDR) of 113.2 dB and an integral nonlinearity (INL) of ±2.5 ppm/full-scale range (FSR). The overall power consumption is 14.88 mW. [ABSTRACT FROM AUTHOR]

An economic-statistical design of the synthetic double sampling (synDS) T2 chart is presented in this study. The cost function is minimized to obtain the optimal design parameters of the synDS T2 chart by incorporating the statistical constraints or the constraints on the average number of samples. An example is provided and a sensitivity analysis is conducted to study the effect of model parameters on the optimal solution of the design. The numerical comparison shows that the synDS T2 chart performs better than the synthetic T2 chart and the multivariate exponentially weighted moving average chart, in terms of the cost. [ABSTRACT FROM AUTHOR]

Many clinical or prevention studies involve missing or censored outcomes. Maximum likelihood (ML) methods provide a conceptually straightforward approach to estimation when the outcome is partially missing. Methods of implementing ML methods range from the simple to the complex, depending on the type of data and the missing-data mechanism. Simple ML methods for ignorable missing-data mechanisms (when data are missing at random) include complete-case analysis, complete-case analysis with covariate adjustment, survival analysis with covariate adjustment, and analysis via propensity-to-be-missing scores. More complex ML methods for ignorable missing-data mechanisms include the analysis of longitudinal dropouts via a marginal model for continuous data or a conditional model for categorical data. A moderately complex ML method for categorical data with a saturated model and either ignorable or nonignorable missing-data mechanisms is a perfect fit analysis, an algebraic method involving closed-form estimates and variances. A complex and flexible ML method with categorical data and either ignorable or nonignorable missing-data mechanisms is the method of composite linear models, a matrix method requiring specialized software. Except for the method of composite linear models, which can involve challenging matrix specifications, the implementation of these ML methods ranges in difficulty from easy to moderate. [ABSTRACT FROM AUTHOR]

This paper proposes a face tracking algorithm based on NTSS and compressed sensing. It uses different search radius and step double sampling, and uses NTSS search method as the target detection method. The algorithm is tested by the international standard face database AFW and MALF video sequences, and the central distance mean algorithm is used to compare the compressed sensing algorithm with the face tracking algorithm based on NTSS compressed sensing.The results are that the face tracking algorithm can reduce the error by 59. 01%. In addition, the NTSS compressed sensing face tracking algorithm has better robustness in face occlusion re-tracking, face geometric transformation and under dark conditions. [ABSTRACT FROM AUTHOR]

Ratio estimators have been used in survey sampling for estimating population mean when the population mean of an auxiliary variable is known. However, as the auxiliary information is not always available we cannot gain any benefit from using ratio estimators to increase the efficiency of the population mean estimator. New ratio estimators for population mean obtained using the quartile function of auxiliary variable( s) using double sampling have been proposed based upon the modified ratio estimators proposed by Subramani and Kumarapandiyan (2012). We propose the use of double sampling in order to estimate an unknown quartile function of an auxiliary variable( s) . A simulation study has been conducted to compare these new ratio estimators with the classical ratio estimator using a percent relative efficiency using double sampling. The results show that the proposed estimators perform better than the classical ratio estimator using double sampling. [ABSTRACT FROM AUTHOR]

This paper proposes an exponential estimator of a population mean under double sampling using one auxiliary variable. The proposed estimator is modified from the estimator of Khatua and Misha (2013) and Ozgul and Cingi (2014). The bias and mean square error of the proposed estimator are derived under the second order of approximation. The efficiency of the proposed estimator is compared with the existing estimators based on mean square error. The results indicate that the proposed estimator is more efficient than others under empirical study with real 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]

Zero-inflated models have been used successfully to describe microbial data with excess of zero-counts. Some sampling plans such as single sampling plan (SSP), resubmitted single sampling plan (RSSP) and quick switching system (QSS) have been developed for the zero-inflated Poisson (ZIP) distribution. Two new plans such as repetitive group sampling (RGS) and multiple dependent state (MDS) sampling are proposed to compare their performance with the existing sampling plans. Additionally, we develop double sampling (DS) and sequential sampling plans for the ZIP data. The plan parameters of the sampling plans are determined by using the unity value approach. The MDS sampling plan is more economical than the other sampling plans, including SSP, RSSP, RGS and QSS, in terms of the required sample size and average sample number in most cases. The probability of a zero in the ZIP data affects the sample size required for all sampling plans. Practical guidance for selecting sampling plans under ZIP is also provided. [ABSTRACT FROM AUTHOR]

This study presents a control chart for monitoring shifts in the covariance matrix of a multivariate normally distributed process. This chart combines the double sampling, variable sample size and variable sampling interval features, and is called the DSVSSI |S| chart. A Markov chain approach is developed to design the DSVSSI |S| chart, by minimizing the average time to signal (ATS), for a specified shift size in the covariance matrix. The DSVSSI |S| chart has a better ATS performance compared to other existing charts. An example is given to illustrate the operation of the DSVSSI |S| chart. [ABSTRACT FROM AUTHOR]

The R chart and the S2 chart are the usual charts for monitoring process dispersion; however, the double sampling ( DS) scheme has only be used with the S2 chart. The difficulty in obtaining the properties of the DS R chart might explain the lack of papers dealing with this type of DS chart. The S2 chart has the advantage of being more efficient than the R chart, but the same is not always observed with their DS versions. Depending on the size of the 2 samples, the DS R chart performs better. The trade-off between operational simplicity and power of detection might lead the practitioner to choose the DS R chart, even with the DS S2 chart signaling faster. [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]

This study proposes a double sampling (DS) Max chart for monitoring shifts in the process mean and standard deviation. The design of the DS Max chart depends on five parameters, i.e. first and second sample sizes, warning limit at Stage 1, upper control limits at Stages 1 and 2. The optimization design of the DS Max chart is conducted using a genetic algorithm by minimizing the average run length. The comparison shows that the DS Max chart performs better than the existing charts in the literature. An example is provided to illustrate the application of the DS Max chart. [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]

A new sampling strategy for forest inventories is presented. The most important difference from the traditional sampling strategies is that auxiliary variables from remote sensing are incorporated into the sampling design. The sample is selected to match population distributions of the auxiliary variables as well as possible. This is achieved by a double sampling approach, where auxiliary variables are extracted for a large first-phase sample. The second selection is done by the local pivotal method and produces an even thinning of the first-phase sample. Thus, we make sure that the selected second-phase sample becomes much more representative of the population than what is possible by the use of traditional designs. The potential of implementing the new strategy for the temporary clusters within the Swedish national forest inventory is evaluated with five auxiliary variables: the geographical coordinates, elevation, predicted tree height, and predicted basal area. The increased representativity that we achieve with the new strategy induces up to 95% reduction of the variance of the sample means of the remote sensing auxiliary variables compared with traditional designs. For this reason, we conclude that the new strategy that will be implemented in the forthcoming Swedish national forest inventory has a great potential to achieve large improvements in estimation of many important forest attributes. [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]