A General Algorithm for Univariate Stratification

This paper presents a general algorithm for constructing strata in a population using  X , a univariate stratification variable known for all the units in the population. Stratum  h  consists of all the units with an  X  value in the interval   [ b _{h −1},  b_h )   . The stratum boundaries   { b_h }   are obtained by minimizing the anticipated sample size for estimating the population total of a survey variable  Y  with a given level of precision. The stratification criterion allows the presence of a take-none and of a take-all stratum. The sample is allocated to the strata using a general rule that features proportional allocation, Neyman allocation, and power allocation as special cases. The optimization can take into account a stratum-specific anticipated non-response and a model for the relationship between the stratification variable  X  and the survey variable  Y . A loglinear model with stratum-specific mortality for  Y  given  X  is presented in detail. Two numerical algorithms for determining the optimal stratum boundaries, attributable to Sethi and Kozak, are compared in a numerical study. Several examples illustrate the stratified designs that can be constructed with the proposed methodology. All the calculations presented in this paper were carried out with stratification , an R package that will be available on CRAN (Comprehensive R Archive Network).     

One-Sided Coverage Intervals for a Proportion Estimated from a Stratified Simple Random Sample

Using an Edgeworth expansion to speed up the asymptotics, we develop one-sided coverage intervals for a proportion based on a stratified simple random sample. To this end, we assume the values of the population units are generated from independent random variables with a common mean within each stratum. These stratum means, in turn, may either be free to vary or are assumed to be equal. The more general assumption is equivalent to a model-free randomization-based framework when finite population correction is ignored. Unlike when an Edgeworth expansion is used to construct one-sided intervals under simple random sampling, it is necessary to estimate the variance of the estimator for the population proportion when the stratum means are allowed to differ. As a result, there may be accuracy gains from replacing the normal  z -score in the Edgeworth expansion with a  t -score.     

Estimating regression coefficients from survey data by asymptotic design-cum-model based approach

Postulating a linear regression of a variable of interest on an auxiliary variable with values of the latter known for all units of a survey population, we consider appropriate ways of choosing a sample and estimating the regression parameters. Recalling Thomsen’s (1978) results on non-existence of ‘design-cum-model’ based minimum variance unbiased estimators of regression coefficients we apply Brewer’s (1979) ‘asymptotic’ analysis to derive ‘asymptotic-design-cummodel’ based optimal estimators assuming large population and sample sizes. A variance estimation procedure is also proposed.     

Stratification of Skewed Populations: A review

When Dalenius provided a set of equations for the determination of stratum boundaries of a single auxiliary variable, that minimise the variance of the Horvitz–Thompson estimator of the mean or total under Neyman allocation for a fixed sample size, he pointed out that, though mathematically correct, those equations are troublesome to solve. Since then there has been a proliferation of approximations of an iterative nature, or otherwise cumbersome, tendered for this problem; many of these approximations assume a uniform distribution within strata, and, in the case of skewed populations, that all strata have the same relative variation. What seems to have been missed is that the combination of these two assumptions offers a much simpler and equally effective method of subdivision for skewed populations; take the stratum boundaries in geometric progression.     

An alternative formulation of Neyman’s smooth goodness of fit tests under composite alternatives

Summary We propose a natural extension of Neyman’s smooth goodness of fit tests under composite hypotheses. The components of our smooth tests are immediate analogues of the corresponding components in the completely specified null case. We show that, when testing for univariate normality, one of our smooth tests is similar to D’Agostino and Pearson’s (1973) statistic; when testing for multivariate normality, our smooth test for skewness is identical to Mardia’s (1970) measure of multivariate skewness; and, when testing for exponentiality, our simplest smooth test is equivalent to Greenwood’s (1946) statistic.     

Queues with hyper-poisson arrivals and bulk exponential service

Summary In this paper the time-dependent solution of a queueing system is discussed under the conditions (i) the units arrive according to Hyper-Poisson distribution withl branches (ii) the queue-discipline is ‘first come first served’ (iii) the Service-time distribution is exponential with maximum capacity ofM units being served at one instant. Some results have been obtained when the waiting space is finite; in particular the probability for service to be idle has been obtained. Also for infinite queueing-space case, the expressions for the state probabilities and the mean queuelength under steady state conditions have been found.     

Estimating sensitive proportions from Warner’s randomized responses in alternative ways restricting to only distinct units sampled

Warner’s (J Am Stat Assoc 60:63–69, 1965) pioneering ‘randomized response’ (RR) technique (RRT), useful in unbiasedly estimating the proportion of people bearing a sensitive characteristic, is based exclusively on simple random sampling (SRS) with replacement (WR). Numerous developments that follow from it also use SRSWR and employ in estimation the mean of the RR’s yielded by each person everytime he/she is drawn in the sample. We examine how the accuracy in estimation alters when instead we use the mean of the RR’s from each distinct person sampled and also alternatively if the Horvitz and Thompson’s (HT, J Am Stat Assoc 47:663–685, 1952) method is employed for an SRSWR eliciting only, a single RR from each. Arnab’s (1999) approach of using repeated RR’s from each distinct person sampled is not taken up here to avoid algebraic complexity. Pathak’s (Sánkhya 24:287–302, 1962) results for direct response (DR) from SRSWR are modified with the use of such RR’s. Through simulations we present relative levels in accuracy in estimation by the above three alternative methods.     

An optional scrambled randomized response technique for practical surveys

Eichhorn and Hayre (J Stat Plan Inference 7:307–316, 1983) introduced the scrambled response technique to gather information on sensitive quantitative variables. Singh and Joarder (Metron 15:151–157, 1997), Gupta et al. (J Stat Plan Inference 100:239–247, 2002) and Bar-Lev et al. (Metrika 60:255–260, 2004) permitted the respondents either to report their true values on the sensitive quantitative variable or the scrambled response and developed the optional randomized response (ORR) technique based on simple random sample with replacement (SRSWR). While developing the ORR procedure, these authors made the assumption that the probability of disclosing the true response or the randomized response (RR) is the same for all the individuals in a population. This is not a very realistic assumption as in practical survey situations the probability of reporting the true value or the RR generally varies from unit to unit. Moreover, if one generalizes the ORR method as developed by these authors relaxing the ‘constant probability’ assumption, the variance of an unbiased estimator for the population total or mean can not be estimated as this involves the unknown parameter, ‘the probability of revealing the true response’. Here we propose a modified ORR procedure for stratified unequal probability sampling after relaxing the assumption of ‘constant probability’ of providing the true response. It is also demonstrated with a numerical exercise that our procedure produces better estimator for a population total than that provided by the method suggested by the earlier authors.     

Nonparametric inference on multivariate versions of Blomqvist’s beta and related measures of tail dependence

We consider nonparametric estimation of multivariate versions of Blomqvist’s beta, also known as the medial correlation coefficient. For a two-dimensional population, the sample version of Blomqvist’s beta describes the proportion of data which fall into the first or third quadrant of a two-way contingency table with cutting points being the sample medians. Asymptotic normality and strong consistency of the estimators are established by means of the empirical copula process, imposing weak conditions on the copula. Though the asymptotic variance takes a complicated form, we are able to derive explicit formulas for large families of copulas. For the copulas of elliptically contoured distributions we obtain a variance stabilizing transformation which is similar to Fisher’s z-transformation. This allows for an explicit construction of asymptotic confidence bands used for hypothesis testing and eases the analysis of asymptotic efficiency. The computational complexity of estimating Blomqvist’s beta corresponds to the sample size n, which is lower than the complexity of most competing dependence measures.      

The problem of optimum stratification and allocation withq=n/N>0

Summary Dalenius/Gurney [1951] published necessary conditions for the stratum boundaries, so that with Neyman's optimal allocation of the sample sizen the variance of the sample mean will become a minimum. They introduced in the variance of the sample mean for the sample sizesn _h the opti mal values according to Neyman and differentiated this variance with respect to the stratum boundaries. Because Neyman's allocation formula yields only feasible solutions forn _h N _h , the conditions ofDalenius result in wrong, i.e. nonfeasible solutions, if one of the restrictionsn _h N _h (h=1 (1) L) is violated.By the example of a logarithmic normal distribution with =0, =1,5 forL=2 the behaviour of the Dalenius-Neyman-minimum and that of the feasible minimum will be shown in dependence on the sampling fractionq=n/N and a critical valueq _c will be given. For valuesq>q _c the Dalenius-Neyman-minimum is no longer feasible.For the same logarithmic normal distribution andL=2 (1) 10 this critical sampling fractionq _c will be given (section 5).For different values of andq the optimal stratum boundaries and sampling fractions are listed in section 6 forL=2;3;4.     

Optimal allocation of the sample size to strata under box constraints

In stratified random sampling without replacement boundary conditions, such as the sample sizes within strata shall not exceed the population sizes in the respective strata, have to be considered. Stenger and Gabler (Metrika, 61:137–156, 2005) have shown a solution that satisfies upper boundaries of sample fractions within the strata. However, in modern applications one may wish to guarantee also minimal sampling fractions within strata in order to allow for reasonable separate estimations. Within this paper, an optimal allocation in the Neyman-Tschuprov sense is developed which satisfies upper and lower bounds of the sample sizes within strata. Further, a stable algorithm is given which ensures optimality. The resulting sample allocation enables users to bound design weights within stratified random sampling while considering optimality in allocation.     

The Dynamics of Efficiency Improving Input Allocation

A dynamic theory of efficiency improving input reallocations is presented which relaxes the assumption that decision-making units have the ability to reorganize their activities instantaneously to achieve efficient input allocations. Explicit account is taken of the decision-making unit’s gradual transition to an efficient input allocation. A balanced panel data set covering 906 U.S. agricultural banks is constructed for the period 1996–2000 and we find that these banks are allocatively efficient but technically inefficient. The explicit solution for the optimal input use finds evidence of gradual transitions for two of the three inputs, with the labor input transition being the slowest. The efficiency improving path finds the firm transitions toward an efficient allocation by simultaneously improving on technical efficiency instantaneously and on allocative efficiency intertemporally, which suggests a non-linear path toward the most efficient allocation.     

Using nonparametric methods in social surveys: an empirical study

The most common form of data for socio-economic studies comes from survey sampling. Often the designs of such surveys are complex and use stratification as a method for selecting sample units. A parametric regression model is widely employed for the analysis of such survey data. However the use of a parametric model to represent the relationship between the variables can be inappropriate. A natural alternative is to adopt a nonparametric approach. In this article we address the problem of estimating the finite population mean under stratified sampling. A new stratified estimator based on nonparametric regression is proposed for stratification with proportional allocation, optimum allocation and post-stratification. We focus on an educational and labor-related context with natural populations to test the proposed nonparametric method. Simulated populations have also been considered to evaluate the practical performance of the proposed method.     

Order Effects in Batteries of Questions

Batteries of questions with identical response items are commonly used in survey research. This paper suggests that question order has the potential to cause systematic positive or negative bias on responses to all questions in a battery. Whilst question order effects have been studied for many decades, almost no attention has been given to this topic. The primary aim is to draw attention to this effect, to demonstrate its possible magnitude, and to discuss a range of mechanisms through which it might occur. These include satisficing, anchoring and cooperativeness. The effect seems apparent in the results of a recent survey. This was a survey of Emergency Department patients presenting to Wollongong Hospital (Australia) with apparently less urgent conditions in 2004. Two samples were taken. Question order was fixed in the first sample (n = 104; response rate RR2 = 94%), but randomised in the second sample (n = 46; response rate RR2 = 96%). Respondents were asked to indicate whether each of 18 reasons for presenting to the ED was a ‘very important reason’ a ‘moderately important reason’ or ‘not a reason’ The mean number of very important reasons selected was 56% higher in the first sample as compared to the second sample.     

Optimum allocation in multivariate stratified random sampling: stochastic matrix mathematical programming

The allocation problem for multivariate stratified random sampling as a problem of stochastic matrix integer mathematical programming is considered, minimizing the estimated covariance matrix of estimated means subject to fixed cost or fixed total sample size. With these aims the asymptotic normality of sample covariance matrices for each strata is established. Some alternative approaches are suggested for its solution. An example is solved by applying the proposed techniques.     

On the efficiency of sampling with varying probabilities and the selection of units with replacement

Summary In this paper the efficiency of sampling with varying probabilities is being compared with the efficiency of some alternative methods, such as simple random sampling, ratio estimate, regression estimate, difference estimate, and stratified sampling with proportionate allocation. The variance formula of the estimated arithmetic mean in the case of sampling with varying probabilities (3) was transformed into (5) and (6) by expanding (3) in Taylor series and the comparisons between the above methods are obtained in Table 1. It was also shown that similar comparisons are possible with multi-stage designs.

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Zusammenfassung Die Wirksamkeit des Stichprobenverfahrens mit ver?nderlichen Wahrscheinlichkeiten wird verglichen mit der Wirksamkeit einiger anderer Verfahren, u.z. der uneingeschr?nkten Zufallsauswahl mit einfacher Hochrechnung und bei Verwendung der Verh?ltnis-, Regressions- und Differenzensch?tzung und der geschichteten Auswahl mit proportionaler Aufteilung. Zu diesem Zweck wurde die Formel für die Varianz des gesch?tzten arithmetischen Mittels im Fall des Verfahrens mit ver?nderlichen Wahrscheinlichkeiten (3) mithilfe einer Taylor-Entwicklung in (5) und (6) transformiert. Die Ergebnisse des Vergleichs sind in Tabelle 1 gegeben. Abschlie?end wird gezeigt, da? ?hnliche Vergleiche auch mit mehrstufigen Verfahren m?glich sind.

     

Subjective Bayesian multivariate stratified sampling from finite populations

Summary In this paper we obtain optimum allocation formula in stratified sampling from a finite population withk characteristics under study using the superpopulation approach put forth byEricson [1969a]. Allocation at the second phase is also considered using information obtained from the first phase. Two different approaches are employed: a Bayesian posterior analysis and a Bayesian preposterior analysis. It is also shown that our allocation formulae for the second phase observations include the results ofMohd. Zubair Khan [1976] andDraper/Guttman [1968a] as special cases when the unknown characteristicX _ij possessed by the (i, j)-th element is scalar valued and the stratum sizes are large.     

Sequential estimation of normal mean under asymmetric loss function with a shrinkage stopping rule

The problem of estimating a normal mean with unknown variance is considered under an asymmetric loss function such that the associated risk is bounded from above by a known quantity. In the absence of a fixed sample size rule, a sequential stopping rule and two sequential estimators of the mean are proposed and second-order asymptotic expansions of their risk functions are derived. It is demonstrated that the sample mean becomes asymptotically inadmissible, being dominated by a shrinkage-type estimator. Also a shrinkage factor is incorporated in the stopping rule and similar inadmissibility results are established. Received September 1997     

Mean squared error efficient estimators of the variance components

Summary Pseudo Bayesian estimators for the variance components based on Jeffrey’s Rule are derived for the mixed balanced incomplete block design and are compared with the usual analysis of variance estimators in terms of mean squared error (MSE) efficiency. Numerical results show that Pseudo-Bayesian estimators are more efficient in numerical results.     

Optimal allocation in J-stage sampling with stratification from finite population

Summary  The known results of optimal allocation in two-stage simple random sampling with stratification from finite population are generalized for J-stages. The optimalization is treated by minimizing the cost function for given variance as well as by minimizing the variance for given cost. It turns out that for both cases the optimal values differ only for the first-stage units and are the same for all the subsequent. The general formula for minimum variance of the unbiased estimator of the population mean per element is given.