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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]

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]

In this paper, we introduce a version of Hayter and Tsui's statistical test with double sampling for the vector mean of a population under multivariate normal assumption. A study showed that this new test was more or as efficient than the well-known Hotelling'sT2with double sampling. Some nice features of Hayter and Tsui's test are its simplicity of implementation and its capability of identifying the errant variables when the null hypothesis is rejected. Taking that into consideration, a new control chart called HTDS is also introduced as a tool to monitor multivariate process vector mean when using double sampling. [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]

In this work, we describe a two-stage sampling design to estimate the infection prevalence in a population. In the first stage, an imperfect diagnostic test was performed on a random sample of the population. In the second stage, a different imperfect test was performed in a stratified random sample of the first sample. To estimate infection prevalence, we assumed conditional independence between the diagnostic tests and develop method of moments estimators based on expectations of the proportions of people with positive and negative results on both tests that are functions of the tests' sensitivity, specificity, and the infection prevalence. A closed-form solution of the estimating equations was obtained assuming a specificity of 100% for both tests. We applied our method to estimate the infection prevalence of visceral leishmaniasis according to two quantitative polymerase chain reaction tests performed on blood samples taken from 4756 patients in northern Ethiopia. The sensitivities of the tests were also estimated, as well as the standard errors of all estimates, using a parametric bootstrap. We also examined the impact of departures from our assumptions of 100% specificity and conditional independence on the estimated prevalence. [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]

Of interest in this paper is the development of a model that uses 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 score and likelihood ratio intervals are generally preferable over the Wald interval. Lastly, the model is applied to a medical data set. [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]

This brief presents a four-level pulse-amplitude modulation (4-PAM) transceiver for latency-sensitive network-on-chip (NoC) applications. The proposed source-synchronous PAM transceiver uses a novel encoder/decoder and dual-sampling technique that transmits and receives two data streams through a shared single-ended channel simultaneously. A conventional PAM transceiver for heterogeneous NoCs is sensitive to a possible latency skew between on-chip intellectual properties during encoding/decoding multiple PAM signals. To mitigate this problem, a dual-sampling technique that makes the link insensitive to PAM latency skews is used to transfer data signals reliably. The proposed 4-PAM transceiver is designed and fabricated using the 130-nm CMOS technology at a 1.2-V supply. The proposed transceiver operates at 6 Gb/s/pin with a power efficiency of 0.7 pJ/b/pin and with a 0.13-mm2 die area. The proposed transceiver achieves a bit error rate of < 10−10, with 27-1 and 215-1 pseudorandom binary sequences at 6 Gb/s/pin. [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]

In this article, an adaptivechart which combines variable sample sizes, sampling intervals, and double sampling features called CVSSIDS control chart is proposed, which uses three different sample sizes, two different sampling intervals, two warning limits, and two different control limits. To evaluate the efficiency of the proposed scheme versus different schemes, average time to signal, average number of samples to signal and average number of observations to signal are utilized. In addition, the minimum ATS values of the proposed chart are computed. Finally, the proposed chart is compared to standard Shewhart (SS) chart and several adaptive control charts. [ABSTRACT FROM AUTHOR]

The steady-state average run length is used to measure the performance of the recently proposed synthetic double sampling [ABSTRACT FROM AUTHOR]

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A combination of double sampling and variable sampling interval (DSVSI) charts can increase the efficiency of signaling small shifts. This study proposes an economic-statistical model for designing joint DSVSI ... and s charts. The proposed approach uses the economic model developed by Duncan in 1956. Research results indicate that the economic-statistical design of the joint DSVSI ... and s charts outperformed traditional economic design approach in detection abilities of process shifts; variable cost (a2), expected costs resulting from defective products when the process was in control (C0), and the expected number of assignable causes (λ) have the major influence on cost E(C′); deviations (δ, γ) significantly influenced cost E(C′); and statistical limitations (AATS*IC; AATS*OOC) do not substantially affect cost E(C′). [ABSTRACT FROM AUTHOR]

Quantiles are more useful descriptive statistics than mean and other moments related statistics because they are less susceptible to long-tailed distributions and outliers. In this study, an auxiliary information based set of Shewhart-type control charts are suggested for an efficient monitoring of process quantiles under simple random sampling and double sampling schemes. The performance of these charting structures is evaluated under bivariate normal and gamma process environments using Average Time to Signal (ATS), Average Extra Quadratic Loss (AEQL) and Average Ratio to ATS (ARATS) as performance measures. We have also examined the effects of contaminated environments on the ATS performance of different quantile based charting structures. Illustrative examples on some selective quantile-type control structures are also provided for procedural details. Finally we have closed with concluding remarks about this study. [ABSTRACT FROM AUTHOR]

Most studies that follow subjects over time are challenged by having some subjects who dropout. Double sampling is a design that selects and devotes resources to intensively pursue and find a subset of these dropouts, then uses data obtained from these to adjust naïve estimates, which are potentially biased by the dropout. Existing methods to estimate survival from double sampling assume a random sample. In limited-resource settings, however, generating accurate estimates using a minimum of resources is important. We propose using double-sampling designs that oversample certain profiles of dropouts as more efficient alternatives to random designs. First, we develop a framework to estimate the survival function under these profile double-sampling designs. We then derive the precision of these designs as a function of the rule for selecting different profiles, in order to identify more efficient designs. We illustrate using data from the United States President's Emergency Plan for AIDS Relief-funded HIV care and treatment program in western Kenya. Our results show why and how more efficient designs should oversample patients with shorter dropout times. Further, our work suggests generalizable practice for more efficient double-sampling designs, which can help maximize efficiency in resource-limited settings. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper gives a detail presentation of a fully pseudo-differential open-loop BiCMOS track-and-hold amplifier (THA) for 9-b operation up to 1 GSample/s. The proposed THA not only uses a double sampling technique to increase the achievable sampling frequency by a factor of two, but also employs a linearization technique to reduce the gain dependence of the THA input stage upon the input level. Moreover, timing mismatch between the clock signals of the two interleaved paths is minimized by means of a timing mismatch insensitive clock generator controlled by a common master sampling clock. The post-layout simulation results using TSMC 75 GHz fT, 0.35-µm SiGe BiCMOS technology show that the proposed architecture achieve a signal to noise and distortion ratio of 53.92 dB, equivalent to the effective number of bits of 8.66-b for 58.11 MHz input frequency at 1 GSample/s. The power dissipation of the whole THA is 161.1 mW. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]