Small domain estimation by empirical bayes and kalman filtering procedures - a case study

An application of empirical Bayes and Kalman filtering tecniques is reported, using live data from Indian Statistical Institute (ISI), Calcutta . to illustrate how initial small domain estimators may be vastly improved upon. A stratified two stage sampling procedure is adopted, allowing selection of first stage units with unequal probabilities but of second stage units with equal probabilities. Standard design-based estimators for domain totals are initialized based on domain specific survey data alone. Strength is then borrowed across domains and from past surveys. The resulting gains in efficacy are numlerically demonstrated, through replicated sampling from official records.     

On group divisible response surface (GDRS) designs

In this paper some experimental situations are identified corresponding to which suitable response surface designs do not exist. A class of response surface designs is introduced to cope with these situations. Their analysis with and without blocking and methods of construction is discussed.     

Variance estimation with randomized response

Presenting a general procedure of eliciting a randomized response (RR) from selected persons in order to estimate the total of a sensitive variable related to a finite survey population, we consider two estimators along with variance estimators treating the case of sampling with probabilities proportional to (known) size measures (PPS) with replacement (WR), drawing analogies with multi-stage sampling and note their relative efficacies.     

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     

On the performance of the nearest proportional to size sampling design

Assuming a super-population model the expected variance of the generalized difference estimator (Basu,1971) based on the nearest proportional to size sampling design introduced by Gabler(1987) is shown to be less than that of the same estimator based on an arbitrary sampling design from which the former design is realized. The former strategy is also shown to fare better than an unbiased ratio-cum-generalized difference estimator based on the nearest proportional to size sampling design in the sense of having less expected design variance under the same model.     

A note on interpenetrating subsamples of unequal sizes drawn with and without replacement

In this article it is shown how one can gain in efficiency in selecting K interpenetrating subsamples of unequal sizes according to Chaudhuri and Adhikary's (1987) scheme rather than selecting them with replacement and considering the estimator based on distinct units as proposed by Bedi(1987).     

A note on non-negative mean square error estimation of regression estimators in randomized response surveys

Generalized regression estimators are considered for the survey population total of a quantitative sensitive variable based on randomized responses. Formulae are presented for ‘non-negative’ estimators of approximate mean square errors of these biased estimators when population and sample sizes are large.     

Effect of urbanization on the dynamics of ecosystem services: An analysis for decision making in Kolkata urban agglomeration

Urban Ecosystems - Urbanization has profound influence on the changes of land use and land cover, which on the other hand exert significant impact on ecosystem services and their values, especially...