Samindranath sengupta | University of Calcutta (original) (raw)
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Papers by Samindranath sengupta
We consider the problem of unbiased estimation of a finite population total related to a sensitiv... more We consider the problem of unbiased estimation of a finite population total related to a sensitive quantitative variable under two scrambled randomized response plans and compare the relative efficiency of the unequal probability sampling strategies due to Horvitz–Thompson (1952) and Murthy (1957) under a super-population model depending on a parameter g. It is shown that for the linear plan the model expected variance is smaller for Murthy’s (1957) strategy if g ≤ 1, while for the multiplicative plan the model expected variance is smaller for the Horvitz-Thompson (1952) strategy if g ≥ 2. We also address the problem of unbiased estimation of the variances of these two sampling strategies under the two randomized response plans and study the non-negative property of the variance estimators.
The problem considered is that of unbiased estimation of the size (N) of a finite closed populati... more The problem considered is that of unbiased estimation of the size (N) of a finite closed population under Capture-mark-Release-Recapture (CMRR) sequential sampling procedure. Borrowing ideas from the Bernoulli sequential estimation and using the notions of ''closed'' and ''pushed-up'' sampling plans, we provide here an unified approach to the problem of unbiased estimation of N and, in particular, give a necessary and sufficient condition for unbiased estimability under an arbitrary stopping rule. The ideas are illustrated with several examples.
Let P be the proportion of individuals in a finite population possessing a sensitive attribute. W... more Let P be the proportion of individuals in a finite population possessing a sensitive attribute. We consider the problem of estimation of the population variance P (1-P) under Warner's (1965) randomized response plan and prove the optimality of a sampling strategy in a class of comparable design unbiased strategies under a super-population model.
We consider the problem of unbiased estimation of a finite population proportion and compare the ... more We consider the problem of unbiased estimation of a finite population proportion and compare the unequal probability sampling strategies due to Hansen-Hurwitz [9], Horvitz-Thompson [12], Rao-Hartley-Cochran [18] and Midzuno-Sen [16, 20] under a super-population model. It is shown that the model expected variance is least for the Midzuno-Sen [16, 20] strategy both when these sampling strategies are based on data obtained from (i) a direct survey and (ii) a randomized response (RR) survey employing some RR technique following a general RR model.
Sankhya
The problem considered is that of unbiased estimation of the size (N) of a finite closed populati... more The problem considered is that of unbiased estimation of the size (N) of a finite closed population under Capture-Mark-Release-Recapture(CMRR) sequential sampling procedure. The existing results are supplemented with various other results and the CMRR procedure is compared with the Negative Binomial and the Negative Hypergeometric sampling schemes in terms of the ASN and the variance of the UMVUE of the population size.
We consider the problem of unbiased estimation of a finite population total related to a sensitiv... more We consider the problem of unbiased estimation of a finite population total related to a sensitive quantitative variable under two scrambled randomized response plans and compare the relative efficiency of the unequal probability sampling strategies due to Horvitz–Thompson (1952) and Murthy (1957) under a super-population model depending on a parameter g. It is shown that for the linear plan the model expected variance is smaller for Murthy’s (1957) strategy if g ≤ 1, while for the multiplicative plan the model expected variance is smaller for the Horvitz-Thompson (1952) strategy if g ≥ 2. We also address the problem of unbiased estimation of the variances of these two sampling strategies under the two randomized response plans and study the non-negative property of the variance estimators.
The problem considered is that of unbiased estimation of the size (N) of a finite closed populati... more The problem considered is that of unbiased estimation of the size (N) of a finite closed population under Capture-mark-Release-Recapture (CMRR) sequential sampling procedure. Borrowing ideas from the Bernoulli sequential estimation and using the notions of ''closed'' and ''pushed-up'' sampling plans, we provide here an unified approach to the problem of unbiased estimation of N and, in particular, give a necessary and sufficient condition for unbiased estimability under an arbitrary stopping rule. The ideas are illustrated with several examples.
Let P be the proportion of individuals in a finite population possessing a sensitive attribute. W... more Let P be the proportion of individuals in a finite population possessing a sensitive attribute. We consider the problem of estimation of the population variance P (1-P) under Warner's (1965) randomized response plan and prove the optimality of a sampling strategy in a class of comparable design unbiased strategies under a super-population model.
We consider the problem of unbiased estimation of a finite population proportion and compare the ... more We consider the problem of unbiased estimation of a finite population proportion and compare the unequal probability sampling strategies due to Hansen-Hurwitz [9], Horvitz-Thompson [12], Rao-Hartley-Cochran [18] and Midzuno-Sen [16, 20] under a super-population model. It is shown that the model expected variance is least for the Midzuno-Sen [16, 20] strategy both when these sampling strategies are based on data obtained from (i) a direct survey and (ii) a randomized response (RR) survey employing some RR technique following a general RR model.
Sankhya
The problem considered is that of unbiased estimation of the size (N) of a finite closed populati... more The problem considered is that of unbiased estimation of the size (N) of a finite closed population under Capture-Mark-Release-Recapture(CMRR) sequential sampling procedure. The existing results are supplemented with various other results and the CMRR procedure is compared with the Negative Binomial and the Negative Hypergeometric sampling schemes in terms of the ASN and the variance of the UMVUE of the population size.