Design-based confidence limits for a distribution function

A method for constructing confidence limits for a distribution function is proposed. This method is a simple modification of the common method based on a normal approximation to the distribution of the estimated distribution function. The methods differ in how the estimated standard errors are used. The coverage properties of the two methods are compared in a simulation study. Coverage probabilities for the proposed method are found to be much closer to the nominal levels, particularly in the tails of the population distribution.     

Simple random sampling with over-replacement

There are several ways to select units with replacement and an equal inclusion expectation. We present a new sampling design called simple random sampling with over-replacement. Its interest lies in the high variance produced for the Horvitz-Thompson estimator. This characteristic could be useful for resampling methods.     

Some calibration estimators for finite population mean in two-stage stratified random sampling

Abstract

In the present article, an effort has been made to develop calibration estimators of the population mean under two-stage stratified random sampling design when auxiliary information is available at primary stage unit (psu) level. The properties of the developed estimators are derived in-terms of design based approximate variance and approximate consistent design based estimator of the variance. Some simulation studies have been conducted to investigate the relative performance of calibration estimator over the usual estimator of the population mean without using auxiliary information in two-stage stratified random sampling. Proposed calibration estimators have outperformed the usual estimator without using auxiliary information.     

On optimality of the horvitz-thompson estimator under a markov process model

For any varying probability sampling design the Horvitz-Thompson (1952) estimator is shown to be optimal within the class of all unbiased estimators of a finite population total under a Markov process model     

Second-order approximations of finite population L-statistics

We construct Edgeworth and empirical Edgeworth approximations to distribution functions of finite population L-statistics and compare their accuracy with that of the normal approximation and the bootstrap approximation in a simulation study.     

Invariance principles for jackknifing U-statistics for finite population sampling and some applications

For simple random sampling (without replacement) from a finite population, suitable stochastic processes are constructed from the entire sequence of jackknife estimators based on smooth functions of U-statistics and these are approximated (in distributions) by some Brownian bridge processes. Strong convergence of the Tukey estimator of the variance of a jackknife U-statistic has been interpreted suitably and established. Some applications of these results in sequential analysis relating to finite population sampling are also considered.     

Estimation of a finite population total in two-stage and stratified sampling under superpopulation models

Optimal sampling strategies which minimise the expected mean square error for a linear design as well as model-design unbiased estimators for a finite population total for two-stage and stratified sampling are obtained under different superpopu1ation models     

Constructing an unbiased estimator of population mean in finite populations using auxiliary information

The objective of this paper is to construct an unbiased estimator (up to order 0(1/n)) of the population mean of the study variatey which is more efficient than the sample mean of the ‘n’ obsrvedy-values. In particular, the unbiased estimators are discussed for the cases of positive and negative correlations of the study variatey and the auxiliary variatex.     

On constructing almost unbiased estimators of finite population mean using transformed auxiliary variable

${\overline y}$ , ratio _R and Tracy et al. (1999) estimators. Received: October 12, 1999; revised version: April 25, 2000     

Sequential point estimation of the means of u-statistics in finite population sampling

In simple random sampling without replacement from a finite population, sequential point estimators of the means of U-statistics are proposed. The proposed procedure is shown to be asymptotically risk efficient in the sense of Starr (Ann. Math. Statist. (1966), 1173-1185)     

Conditional inference in finite population sampling under a calibration model

Several estimators, including the classical and the regression estimators of finite population mean, are compared, both theoretically and empirically, under a calibration model, where the dependent variable(y), and not the independent variable(x), can be observed for all units of the finite population. It is shown asymptotically that when conditioned on x, the bias of the classical estimator may be much smaller than that of the regression estimators; whereas when conditioned on y, the regression estimator may have much smaller conditional bias than the classical estimator. Since all the y's(not x's) can be observed, it seems appropriate to make comparison under the conditional distribution of each estimator with y fixed. In this case, the regression estimator has smaller variance, smaller conditional bias, and the conditional coverage probability closer to its nominal level     

On the comparison of Warner's and unrelated question randomized response plans for estimating sensitive finite population proportions

We consider the problem of estimation of a finite population proportion (P) related to a sensitive attribute under Warner's (1965 Warner, S.L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60:63–69.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) randomized response plan and the unrelated question plan due to Horvitz et al. (1967 Warner, S.L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60:63–69.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) and prove that for a given probability sampling design, given any linear unbiased estimator (LUE) of P based on Warner's (1965 Warner, S.L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60:63–69.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) plan with any given value of the plan parameter, there exists an LUE of P based on the unrelated question plan with a uniformly smaller variance for suitable choices of the plan parameters. Assuming that only the attribute is sensitive but its complement is innocuous, the same is also shown to be true when the plan parameters for the two plans are so chosen so that both offer the same specified level of privacy.     

Ratio estimators based on a ranked set sample in a finite population setting

This paper considers the ratio estimator in a finite population setting in a ranked set sampling (RSS) design, where the sample is constructed either with or without replacement policies. It is shown that the proposed ratio estimator is slightly biased, but the amount of bias is smaller than the amount of bias of a simple random sample (SRS) ratio estimator. For the proposed ratio estimator, the paper provides explicit expressions for its mean square error and precision relative to the other competing estimators. It is shown that the new estimator has a substantial amount of improvement in efficiency with respect to SRS estimator. The proposed estimator is applied to two different finite population settings to estimate population mean.     

On some properties of variable size simple random sampling and a limit theorem

This paper introduces a sampling plan for finite populations herein called “variable size simple random sampling” and compares properties of estimators based on it with results from the usual fixed size simple random sampling without replacement. Necessary and sufficient conditions (in the spirit of Hajek (1960)) for the limiting distribution of the sample total (or sample mean) to be normal are given.     

Estimating the variance of a normal population by utilizing the information in a sample from a second related normal population

A class of estimators of the variance σ₁ ² of a normal population is introduced, by utilization the information in a sample from a second normal population with different mean and variance σ₂ ², under the restriction that σ₁ ²?≤?σ₂ ². Simulation results indicate that some members of this class are more efficient than the usual minimum variance unbiased estimator (MVUE) of σ₁ ², Stein estimator and Mehta and Gurland estimator. The case of known and unknown means are considered.     

Estimating the permanent by importance sampling from a finite population

The Calculation of the permanent of a matrix is an extremely difficult task. Indeed it belongs to the class of hard counting problems denoted #P complete and hence any algorithm to compute the permanent must run in exponential time in the order of the matrix. Attention, therefore, has concentrated on Monte Carlo algorithms to estimate permanents with considerable emphasis on deriving randomised polynomial time algorithms. Interest in this area hs largely stemmed from problems in combibatorial enumeration, for instance the permanent of a square(0,1) matrix gives the number of perfect matchings in a bipartite graph.     

Approximate optimal allocation in repeated sampling from a finite population

Samples of size n are drawn from a finite population on each of two occasions. On the first occasion a variate x is measured, and on the second a variate y. In estimating the population mean of y, the variance of the best linear unbiased combination of means for matched and unmatched samples is itself minimized, with respect to the sampling design on the second occasion, by a certain degree of matching. This optimal allocation depends on the population correlation coefficient, which previous authors have assumed known. We estimate the correlation from an initial matched sample, then an approximately optimal allocation is completed and an estimator formed which, under a bivariate normal superpopulation model, has model expected mean square error equal, apart from an error of order n^-2, to the minimum enjoyed by any linear, unbiased estimator.     

Admissible unbiased estimation of finite population variance under a randomized response model

We consider the problem of estimation of a finite population variance related to a sensitive character under a randomized response model and prove (i) the admissibility of an estimator for a given sampling design in a class of quadratic unbiased estimators and (ii) the admissibility of a sampling strategy in a class of comparable quadratic unbiased strategies.     

On uncertainty of a proportion from a stratified random sample of a small population

This article considers the uncertainty of a proportion based on a stratified random sample of a small population. Using the hypergeometric distribution, a Clopper–Pearson type upper confidence bound is presented. Another frequentist approach that uses the estimated variance of the proportion estimator is also considered as well as a Bayesian alternative. These methods are demonstrated with an illustrative example. Some aspects of planning, that is, the impact of specified strata sample sizes, on uncertainty are studied through a simulation study.     

Variance Estimation and Asymptotic Confidence Bands for the Mean Estimator of Sampled Functional Data with High Entropy Unequal Probability Sampling Designs

For fixed size sampling designs with high entropy, it is well known that the variance of the Horvitz–Thompson estimator can be approximated by the Hájek formula. The interest of this asymptotic variance approximation is that it only involves the first order inclusion probabilities of the statistical units. We extend this variance formula when the variable under study is functional, and we prove, under general conditions on the regularity of the individual trajectories and the sampling design, that we can get a uniformly convergent estimator of the variance function of the Horvitz–Thompson estimator of the mean function. Rates of convergence to the true variance function are given for the rejective sampling. We deduce, under conditions on the entropy of the sampling design, that it is possible to build confidence bands whose coverage is asymptotically the desired one via simulation of Gaussian processes with variance function given by the Hájek formula. Finally, the accuracy of the proposed variance estimator is evaluated on samples of electricity consumption data measured every half an hour over a period of 1 week.