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This article deals with the problems of efficient estimation of population variance in two-phase (double) sampling. Using information on two auxiliary variables, a class of chain exponential to regression type estimators has been proposed and its properties are studied under two different structures of two-phase sampling. Superiority of suggested class of estimators over some existing ones is established through numerical illustrations. Suitable recommendations to the survey statistician are also made.

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]

Specially designed forest damage inventories, directed to areas with potential or suspected damage, are performed in many countries. In this study, we evaluate a new approach for damage inventories in which auxiliary data are used for the sample selection with the recently introduced local pivotal sampling design. With this design, a sample that is well spread in the space of the auxiliary variables is obtained. We applied Monte Carlo sampling simulation to evaluate whether this sampling design leads to more precise estimates compared with commonly applied baseline methods. The evaluations were performed using different damage scenarios and different simulated relationships between the auxiliary data and the actual damages. The local pivotal method was found to be more efficient than simple random sampling in all scenarios, and depending on the allocation of the sample and the properties of the auxiliary data, it sometimes outperformed two-phase sampling for stratification. Thus, the local pivotal method may be a valuable tool to cost-efficiently assess the magnitude of forest damage once outbreaks have been detected in a forest region. [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]

The double sampling (DS) X bar chart, one of the most widely-used charting methods, is superior for detecting small and moderate shifts in the process mean. In a right skewed run length distribution, the median run length (MRL) provides a more credible representation of the central tendency than the average run length (ARL), as the mean is greater than the median. In this paper, therefore, MRL is used as the performance criterion instead of the traditional ARL. Generally, the performance of the DS X bar chart is investigated under the assumption of known process parameters. In practice, these parameters are usually estimated from an in-control reference Phase-I dataset. Since the performance of the DS X bar chart is significantly affected by estimation errors, we study the effects of parameter estimation on the MRL-based DS X bar chart when the in-control average sample size is minimised. This study reveals that more than 80 samples are required for the MRL-based DS X bar chart with estimated parameters to perform more favourably than the corresponding chart with known parameters.

Standard Shewhart process control chart has been widely used, but it is not sensitive in detecting small shift. A number of alternatives have been proposed to improve the capability of control chart. The double sampling (DS) control chart is aimed at improving the capability to detect any small shift out-of control condition by observing the second sample without interruption. The capabilities of DS control chart were measured as the expected sample size (as a measure of inspection cost) or the control chart power (as a measure for customer risk). Optimization of these criteria is used to determine the control limits. In this paper, we optimize both producer and customer risks under a certain expected number of sample sizes as the constraint. Comparing the result to the previous procedure that only optimize customer risk, the proposed optimization procedure gives the same first control limit but smaller second control limits with higher value of control chart power.

A class of estimator based on dual to ratio and dual to product-type estimator in double sampling for estimating finite population mean is proposed. The bias and mean square error of the proposed estimator are obtained in two different cases. A comparative study has been made with usual estimators, dual to ratio estimator, dual to product type estimator in double sampling in two different cases. The proposed estimator is found to be an improvement over the other estimators as special cases.

BACKGROUND: Meta-research can involve manual retrieval and evaluation of research, which is resource intensive. Creation of high throughput methods (e.g., search heuristics, crowdsourcing) has improved feasibility of large meta-research questions, but possibly at the cost of accuracy. OBJECTIVE: To evaluate the use of double sampling combined with multiple imputation (DS+MI) to address meta-research questions, using as an example adherence of PubMed entries to two simple Consolidated Standards of Reporting Trials (CONSORT) guidelines for titles and abstracts. METHODS: For the DS large sample, we retrieved all PubMed entries satisfying the filters: RCT; human; abstract available; and English language (n=322,107). For the DS subsample, we randomly sampled 500 entries from the large sample. The large sample was evaluated with a lower rigor, higher throughput (RLOTHI) method using search heuristics, while the subsample was evaluated using a higher rigor, lower throughput (RHITLO) human rating method. Multiple imputation of the missing-completely-at-random RHITLO data for the large sample was informed by: RHITLO data from the subsample; RLOTHI data from the large sample; whether a study was an RCT; and country and year of publication. RESULTS: The RHITLO and RLOTHI methods in the subsample largely agreed (phi coefficients: title=1.00, abstract=0.92). Compliance with abstract and title criteria has increased over time, with non-US countries improving more rapidly. DS+MI logistic regression estimates were more precise than subsample estimates (e.g., 95% CI for change in title and abstract compliance by Year: subsample RHITLO 1.050-1.174 vs. DS+MI 1.082-1.151). As evidence of improved accuracy, DS+MI coefficient estimates were closer to RHITLO than the large sample RLOTHI. CONCLUSIONS: Our results support our hypothesis that DS+MI would result in improved precision and accuracy. This method is flexible and may provide a practical way to examine large corpora of literature.

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]

This paper presents a 9-bit 1.8 GS/s pipelined analog-to-digital converter (ADC) using open-loop amplifiers. In this ADC, open-loop amplifiers are used as residue amplifiers to increase the sampling rate of the ADC with relatively low power consumption. A linearization technique is proposed to suppress the SNDR decrease caused by the nonlinearity of open-loop amplifiers. The attenuation in the capacitor digital-to-analog converter (CDAC) is utilized to calibrate the gain error of the pipelined stages. In addition, top-plate sampling is proposed to further enhance the power efficiency of the residue amplifiers. With these techniques, the ADC achieves a high sampling rate and high power efficiency. A prototype of the ADC is fabricated in 65 nm CMOS technology. An SNDR of 47 dB and a FoM of 134 fJ/conversion-step is achieved at a sampling rate of 1.8 GS/s with 900 MHz input, while consuming 44 mW from a 1.2 V supply. [ABSTRACT FROM PUBLISHER]