Compromised imputation in survey sampling

In this paper, a compromised imputation procedure has been suggested. The estimator of mean obtained from compromised imputation remains better than the estimators obtained from ratio method of imputation and mean method of imputation. An idea to form “Warm Deck Method” of imputation has also been suggested. Received: July 1998     

Qualitative sampling in a multi-method survey

Because method triangulation is able to reduce disadvantages of one method by using another one, it has been demanded in social science for many years. Nevertheless, it is seldom applied in practical research. To show the importance of multi-method-research for improving the results of social science investigation, one possibility of method triangulation and its execution is presented. Out of a representative youth survey about sexual behavior and AIDS-prevention with 1500 German respondents a sample of 15–20 respondents for depth-interviews is extracted by combining clusteranalysis and random-sampling. The aim of that multi-method procedure is to get more detailed information about subgroup specific topics and problems. At first the paper shows the necessity of method triangulation and its application in our survey. After that the procedure and the results of the clusteranalysis are presented. In the last chapter the sampling and its results are discribed and discussed.     

A conditional minimax approach in survey sampling

Summary Applying the usual minimax criterion in finite sampling theory yields complicated solutions except the parameter space has certain invariance properties. A conditional minimax criterion is suggested. After a sample is selected it is reasonable to seek an estimator that has good properties (e.g. minimaxity) for that sample. Explicit solutions are given in the case where the parameter space is described by quadratic forms.     

Loss functions and admissible estimators in survey sampling

Summary Consider simple random sampling (without replacement) of a fixed size and lett ₀ be the sample mean, i.e. the arithmetic mean of all variate values observed in the sample. The class {t ₀: real} of estimators for the population mean (i.e. The arithmetic mean of all variate values) then surely is of interest.We discuss different types of variates, in particular variates with positive values only. For these variates the usual square error loss gives rise to a strange class of admissible estimators. An other type of loss functions seems far more appropriate. For this (logarithmic) type, t ₀ is admissible iff =1. We prove that there exists no other type of loss functions with the property that the unbiased estimatort ₀ is the only admissible element of the class {t ₀: >0}.

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Zusammenfassung Bei fest vorgegebenem Stichprobenumfang werde uneingeschränkt zufällig ausgewählt (ohne Zurücklegen);t ₀ bezeichne das Stichprobenmittel, d.h. das arithmetische Mittel aller Stichprobenbeobachtungen des Untersuchungsmerkmals. Von besonderen Interesse ist dann zweifellos die Klasse {t ₀: reell} von Schätzfunktionen für das Gesamtmittel, d.h. für das arithmetische Mittel aller Ausprägungen des Untersuchungsmerkmals.Wir betrachten verschiedene Typen von Untersuchungsmerkmalen, insbesondere Merkmale, die nur positive Ausprägungen besitzen. Für diese Merkmale führt die Verwendung der üblichen quadratischen verlustfunktion zu einer sehr merkwürdigen Klasse zulässiger Schätzfunktionen. Ein anderer Typ von Verlustfunktionen erscheint weit eher angebracht. Für diesen (logarithmischen) Typ ist t ₀ genaudann zulässig, wenn =1 gilt. Wir beweisen, daß für keinen anderen Typ von Verlustfunktionent ₀ das einzige zulässige Element der Klasse {t ₀:>0} ist.

     

Model assisted survey sampling strategy in two phases

Postulating a super-population regression model connecting a size variable, a cheaply measurable variable and an expensively observable variable of interest, an asymptotically optimal double sampling strategy to estimate the survey population total of the third variable is specified. To render it practicable, unknown model-parameters in the optimal estimator are replaced by appropriate statistics. The resulting generalized regression estimator is then shown to have a model-cum-asymptotic design based expected square error equal to that of the asymptotically optimum estimator itself. An estimator for design variance of the estimator is also proposed.     

Asymptotic expansion of the minimax value in survey sampling

Summary Suppose that a real numbery _u is associated with each unitu of a populationU and that the functiony:u →y _u onU is known to be an element of the parameter space Θ. The statistician has to select a samples ⊂U ofn units and to employy _u;u ∈s to estimate the arithmetic mean of ally _u,u ∈U. The performance of such a strategy is assessed by its mean square error or, more simply, by the supremum of the mean square error. This supremum cannot be determined exactly for the parameter space of Scott/Smith (1975). We propose, therefore, an asymptotic approximation; this approximation is based on the assumption, that the sample sizen is fixed and that linear estimators have to be used.     

Influence diagnostic in survey sampling: Estimating the conditional bias

The conditional bias has been proposed by Moreno Rebollo et al. (1999) as an influence diagnostic in survey sampling, when the inference is based on the randomization distribution generated by a random sampling. The conditional bias is a population parameter. So, from an applied point of view, it must be estimated. In this paper, we propose an estimator of the conditional bias and we study conditions that guarantee its unbiasedness. The results are applied in a Simple Random Sampling and in a Proportional Probability Aggregated Size Sampling, when the ratio estimator is used. Received October 2000     

Estimation of finite population variance using random non-response in survey sampling

In this paper, an estimator of finite population variance proposed by Isaki (1983) is studied under the two different situations of random non-response suggested by Tracy and Osahan (1994). A distribution is proposed for the number of sampling units on which information could not be obtained due to random non-response. The estimators for the mean square errors of the proposed strategies are also suggested. This paper was written while both authors were members of the Dept. of Econometrics, Monash University, Clayton 3168, Australia. This paper was presented on SISC—1996, Sydney, Australia. The opinions and results discussed in this paper are of authors and not necessarily of their institutes.     

Estimation of a proportion in survey sampling using the logratio approach

The estimation of a mean of a proportion is a frequent task in statistical survey analysis, and often such ratios are estimated from compositions such as income components, wage components, tax components, etc. In practice, the weighted arithmetic mean is regularly used to estimate the center of the data. However, this estimator is not appropriate if the ratios are estimated from compositions, because the sample space of compositional data is the simplex and not the usual Euclidean space. We demonstrate that the weighted geometric mean is useful for this purpose. Even for different sampling designs, the weighted geometric mean shows excellent behavior.     

General framework for the rotation of units in repeated survey sampling

Coordination of probabilistic samples is a challenging theoretical problem faced by statistical institutes. One of their aims is to obtain good estimates for each wave while spreading the response burden across the entire population. There is a collection of existing solutions that try to attend to these needs. These solutions, which were developed independently, are integrated in a general framework and their corresponding longitudinal designs are computed. The properties of these longitudinal designs are discussed. It is also noted that there is an antagonism between a good rotation and control over the cross-sectional sampling design. A compromise needs to be reached between the quality of the sample coordination, which appears to be optimal for a systematic longitudinal sampling design, and the freedom of choice of the cross-sectional design. In order to reach such a compromise, an algorithm that uses a new method of longitudinal sampling is proposed.     

On generalized estimators in sampling from finite populations

Summary A comparative study of the generalized estimators proposed byKoop andMurthy is made.

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Zusammenfassung In der vorliegenden Arbeit vergleicht der Autor verschiedene vonKoop undMurthy beschriebene Estimatoren.

     

On ratio and product methods of estimation in sampling

Abstract  In sample surveys the ratio (product) method of estimation is quite effective when there is a positively (negatively) high correlation between the study variate and an auxiliary variate on which supplementary information is available. This paper considers four estimators suited for cases where these correlations are only moderate and gives a rule of thumb for choosing among these and the traditional estimators. Such a choice needs a good guess of the interval containing a certain parameter k , which may not be hard in survey practice. A numerical example has been included for the case of positive correlation for illustration. An extension for using multiauxiliary information is also considered.     

Estimation of ratio of population means in survey sampling when some observations are missing

This paper considers the estimation of the ratio of population means when some observations are missing. Four estimators are presented and their bias and mean square error properties are studied. Received: June 1998     

On a use of stratified sampling in optimal estimation

Summary Postulating an intra-class correlation structure for a finite population vector of variate — values optimal stratified and unstratified strategies for estimating the total are identified and relative efficiencies noted. Higher efficiency of stratified over unstratified sampling is demonstrated for usual estimators in ppswr case, in particular. In the uncorrelated case appropriately chosen two-stage strategies are observed to fare at par with optimal stratified ones.     

On the relative precision of several allocations in stratified sampling

MOORS (1973) gave lower bounds for the relative precision of different allocations compared to Neyman allocation in stratified sampling. These results are extended here to the relative precision with respect to optimal allocation. A result of COCHRAN (1977) is generalized.     

On Bayesian optimum allocation in some two phase sampling problems

Summary For estimating certain parametric functions, we consider the problem of allocatingN _i, the size of the sample from theith population,i=1,2,...,k, at the second phase of sampling of a two phase sampling procedure, given that we taken _i observations from the population at the first phase. We consider that the observations from theith population follow the exponential distribution with mean _i,i=1,2,...,k, and the functions to be estimated are (i) (d_i/_i) and (ii) (d_ii). When the total cost of sampling at the second phase is c _iN_i and is fixed, allocations using the Bayes approach are obtained so that the estimation is as precise as is possible.     

浅论工程地质勘查

文章主要通过对北京大学第三医院改扩建门急诊医技楼与运动医学楼护坡、降水及土方开挖工程项目来具体说明工程地质勘查的重要性。     

Two new models for survey sampling with sensitive characteristic: design and analysis

Sensitive topics or highly personal questions are often being asked in medical, psychological and sociological surveys. This paper proposes two new models (namely, the triangular and crosswise models) for survey sampling with the sensitive characteristics. We derive the maximum likelihood estimates (MLEs) and large-sample confidence intervals for the proportion of persons with sensitive characteristic. The modified MLEs and their asymptotic properties are developed. Under certain optimality criteria, the designs for the cooperative parameter are provided and the sample size formulas are given. We compare the efficiency of the two models based on the variance criterion. The proposed models have four advantages: neither model requires randomizing device, the models are easy to be implemented for both interviewer and interviewee, the interviewee does not face any sensitive questions, and both models can be applied to both face-to-face personal interviews and mail questionnaires.