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Arijit Chaudhuri

Researcher at Indian Statistical Institute

Publications -  110
Citations -  2482

Arijit Chaudhuri is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Population & Estimator. The author has an hindex of 21, co-authored 107 publications receiving 2288 citations.

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A note on handling linear randomized response

TL;DR: In this article, it is shown that the method given by Chaudhuri (1987) to estimate a finite population total of a sensitive variable from randomized responses (RR) obtained through a varying probability sample from a finite populations does not directly apply but needs a modification to cover a case where RR is obtained as a random linear compound of variate-values.
Journal Article

Extending sitter's mirror-match bootstrap to cover Rao-Hartley-Cochran sampling in two-stages with simulated illustrations

Arijit Chaudhuri, +1 more
- 01 Jan 2004 - 
TL;DR: In this article, a practical problem of estimating the total area under cultivation in Indian districts is addressed by two-stage sampling with unequal selection-probabilities, and the accuracy in estimation bootstrap technique is employed in constructing confidence intervals and simulation-based performance criteria are evaluated from live-data as shown for competitive procedures.
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A finite population quantile estimation by unequal probability sampling

TL;DR: Using a model-assisted approach, the authors studies asymptotically design-unbiased (ADU) estimation of a population distribution function and extends to derive an asymPTotic and approximate unbounded distribution function.
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Asymptotic design-cum-model based estimation of variances of estimated linear regression coefficients in survey sampling with unequal probabilities

TL;DR: In this paper, a superpopulation linear regression model for a variable of interest on an auxiliary variable was proposed and the design-based estimation of regression coefficients on drawing a sample with unequal probabilities from a survey population was considered.
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Further improvements on unrelated characteristic models in randomized response techniques

TL;DR: Unrelated characteristics model (URL) as mentioned in this paper is a type of randomized response technique (RRT) used to estimate finite population proportion of individuals bearing such a sensitive characteristic whose characteristics were selected by a random response technique.