Sujay Mukhuti

Bio: Sujay Mukhuti is an academic researcher from St. Xavier's College-Autonomous, Mumbai. The author has contributed to research in topics: Best linear unbiased prediction & Minimum-variance unbiased estimator. The author has an hindex of 2, co-authored 2 publications receiving 49 citations.

Unbiased estimation of P( X > Y )using ranked set sample data

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.

Unbiased Estimation of P(X > Y) for Exponential Populations Using Order Statistics with Application in Ranked Set Sampling

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.

Survival Distributions: Reliability Applications in the Biomedical Sciences

Abstract: (1976). Survival Distributions: Reliability Applications in the Biomedical Sciences. Technometrics: Vol. 18, No. 4, pp. 501-501.

Ranked Set Sampling: Its Relevance and Impact on Statistical Inference

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.

Estimation of P(X < Y) using ranked set sampling for the Weibull distribution

Abstract: This paper deals with making inferences regarding system reliability when the distribution of the stress X and the strength Y are independent Weibull. In the literature, estimators based on simple random sampling (SRS) are widely used in estimating R. However, in recent years, ranked set sampling (RSS) has become popular in performing statistical inference. We, therefore, obtain the estimators of R based on RSS using maximum likelihood (ML) and modified maximum likelihood (MML) methodologies. The performances of the proposed estimators are compared with their counterparts based on SRS using Monte Carlo simulation. The simulation results show that the proposed estimators are more preferable than the estimators based on SRS in terms of efficiency. In addition, under the assumption of imperfect ranking the efficiencies of the ML and the MML estimators of R, based on RSS, are compared and the ML estimator of R is found to be more efficient. Finally, a real data-set is analysed to demonstrate the imple...

Inferences on stress–strength reliability based on ranked set sampling data in case of Lindley distribution

Abstract: In this study, we consider point and interval estimation of stress–strength reliability R=P(X

A new reliability measure in ranked set sampling

Abstract: In this article, a dynamic reliability measure based on ranked set sampling is introduced, and its properties are investigated in theory and simulation. The results support the preference of the suggested index over the analogous one in simple random sampling. A data set from an agricultural experiment is analyzed for illustration.