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S. Sengupta

Bio: S. Sengupta is an academic researcher from University of Calcutta. The author has contributed to research in topics: Population & Sampling (statistics). The author has an hindex of 9, co-authored 33 publications receiving 157 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, the problem of unbiased estimation of P[X>Y] using ranked set sample data for two independent random variables X and Y with unknown probability distributions was considered, and it was proved that the ranked set samples provided an unbiased estimator with smaller variance as compared with simple random samples of same sizes.
Abstract: The problem considered is that of an unbiased estimation of P[X>Y] using ranked set sample data for two independent random variables X and Y with unknown probability distributions. Postulating a model for imperfect ranking, it is proved that the ranked set samples provide an unbiased estimator with smaller variance as compared with simple random samples of same sizes, even when the rankings are imperfect. It is further shown that the ranked set sampling provides maximum efficiency when the rankings are perfect.

33 citations

Journal ArticleDOI
TL;DR: In this article, the problem of unbiased estimation of P[X>Y] = θ for two independent exponentially distributed random variables X and Y is addressed and a unique unbiased estimator of θ based on a single pair of order statistics obtained from two independent random samples from the two populations is presented.
Abstract: This paper addresses the problem of unbiased estimation of P[X > Y] = θ for two independent exponentially distributed random variables X and Y. We present (unique) unbiased estimator of θ based on a single pair of order statistics obtained from two independent random samples from the two populations. We also indicate how this estimator can be utilized to obtain unbiased estimators of θ when only a few selected order statistics are available from the two random samples as well as when the samples are selected by an alternative procedure known as ranked set sampling. It is proved that for ranked set samples of size two, the proposed estimator is uniformly better than the conventional non-parametric unbiased estimator and further, a modified ranked set sampling procedure provides an unbiased estimator even better than the proposed estimator.

23 citations

Journal ArticleDOI
TL;DR: In this article, different superpopulation models for a finite population with linear trend were proposed and optimal sampling strategies for estimating the finite population mean within some classes of unbiased strategies were proposed.
Abstract: SUMMARY Postulating different superpopulation models for a finite population with linear trend, we suggest optimal sampling strategies for estimating the finite population mean within some classes of unbiased strategies.

16 citations

Journal ArticleDOI
TL;DR: In this article, the problem of unbiased estimation of the variance of an exponential distribution using a ranked set sample (RSS) was considered and the authors proposed some unbiased estimators each of which is better than the non-parametric minimum variance quadratic unbiased estimator based on a balanced ranked subset sample as well as the uniformly minimum variance unbiased estimulator based on simple random sample (SRS) of the same size.

14 citations

Journal ArticleDOI
TL;DR: In this paper, the problem of estimating a finite population mean or proportion related to a sensitive character is considered under a randomized response model, and some non-existence, admissibility and optimality results have been derived from the known results in the open set-up.

11 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, survival distributions for reliability applications in the Biomedical Sciences are discussed, with a focus on the reliability of the distribution of survival distributions in the field of bio-medical applications.
Abstract: (1976). Survival Distributions: Reliability Applications in the Biomedical Sciences. Technometrics: Vol. 18, No. 4, pp. 501-501.

513 citations

PatentDOI
15 Mar 2007
TL;DR: This invention is generally directed to one or more systems or methods relating to social network analysis and the analysis of personal-communication-network data.
Abstract: This invention is generally directed to one or more systems or methods relating to social network analysis. More specifically, this invention is generally directed to one or more systems or methods relating to personal communication networks and the analysis of personal-communication-network data.

136 citations

Journal ArticleDOI
TL;DR: In this paper, a general class of sampling methods without replacement and with unequal probabilities is proposed, which consists of splitting the inclusion probability vector into several new inclusion probability vectors, one of these vectors is chosen randomly; thus, the initial problem is reduced to another sampling problem with unequal probability.
Abstract: SUMMARY A very general class of sampling methods without replacement and with unequal probabilities is proposed. It consists of splitting the inclusion probability vector into several new inclusion probability vectors. One of these vectors is chosen randomly; thus, the initial problem is reduced to another sampling problem with unequal probabilities. This splitting is then repeated on these new vectors of inclusion probabilities; at each step, the sampling problem is reduced to a simpler problem. The simplicity of this technique allows one to generate easily new sampling procedures with unequal probabilities. The splitting method also generalises well-known methods such as the Midzuno method, the elimination procedure and the Chao procedure. Next, a sufficient condition is given in order that a splitting method satisfies the Sen-Yates-Grundy condition. Finally, it is shown that the elimination procedure satisfies the Gabler sufficient condition.

136 citations

Journal ArticleDOI
TL;DR: An introduction to the basic concepts underlying ranked set sampling, in general, with specific illustrations from the one- and two-sample settings are provided and targeted discussion of the many options available to the researcher within the RSS paradigm is discussed.
Abstract: Ranked set sampling (RSS) is an approach to data collection and analysis that continues to stimulate substantial methodological research. It has spawned a number of related methodologies that are active research arenas as well, and it is finally beginning to find its way into significant applications beyond its initial agricultural-based birth in the seminal paper by McIntyre (1952). In this paper, we provide an introduction to the basic concepts underlying ranked set sampling, in general, with specific illustrations from the one- and two-sample settings. Emphasis is on the breadth of the ranked set sampling approach, with targeted discussion of the many options available to the researcher within the RSS paradigm. The paper also provides a thorough bibliography of the current state of the field and introduces the reader to some of the most promising new methodological extensions of the RSS approach to statistical data analysis.

109 citations

Journal ArticleDOI
TL;DR: Basic Statistics and Data Analysis is not suitable for students majoring in math, science, and engineering, because of the sparse coverage of statistical topics applicable to these fields.
Abstract: (2005). Ranked Set Sampling: Theory and Applications. Technometrics: Vol. 47, No. 1, pp. 100-101.

104 citations