The purpose of the present article is to take stock of a recent exchange in Organizational Research Methods between critics and proponents of partial least squares path modeling (PLS-PM). The two t...

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Reflections on Partial Least Squares Path Modeling

A potential inflating effect in estimation models: Cautionary evidence from comparing performance enhancing drug and herbal hormonal supplement use estimates

In this study, we propose a new improved estimator of population mean for the sensitive variable in the presence of measurement error under simple and stratified random sampling. This estimator accounts the auxiliary information as well as the ranks of the auxiliary variable. From theoretical and numerical studies it is shown that a new improved estimator performs better than the existing estimators under study.

Estimation of finite population mean for a sensitive variable using dual auxiliary information in the presence of measurement errors.

In this paper, a general class of estimators is proposed for estimating the finite population mean for sensitive variable, in the presence of measurement error and non-response in simple random sampling. Expressions for bias and mean square error up to first order of approximation, are derived. Impact of measurement errors is examined using real data sets, including the survey conducted at Quaid-i-Azam University, Islamabad. Simulated data sets are also used to observe the performance of the proposed estimators in comparison to some other estimators. We obtain the empirical bias and MSE values for the proposed and the competing estimators.

/pdf/a-generalized-class-of-estimators-for-sensitive-variable-in-1ppgpb4v.pdf

A generalized class of estimators for sensitive variable in the presence of measurement error and non-response

The present paper suggests an interesting and useful ramification of the unrelated randomized response model due to Pal and Singh (2012) [A new unrelated question randomized response model. Statist...

A New Efficient Unrelated Randomized Response Model

In this paper, we extend the work of Gjestvang and Singh [A new randomized response model, J R Statist Soc Ser B (Methodological) 68 (2006), pp 523–530] to propose a new unrelated question randomized response model that can be used for any sampling scheme The interesting thing is that the estimator based on one sample is free from the use of known proportion of an unrelated character, unlike Horvitz et al [The unrelated question randomized response model, Social Statistics Section, Proceedings of the American Statistical Association, 1967, pp 65–72], Greenberg et al [The unrelated question randomized response model: Theoretical framework, J Amer Statist Assoc 64 (1969), pp 520–539] and Mangat et al [An improved unrelated question randomized response strategy, Calcutta Statist Assoc Bull 42 (1992), pp 167–168] models The relative efficiency of the proposed model with respect to the existing competitors has been studied

A new unrelated question randomized response model

On efficacy of empirical Bayes estimation of a finite population mean of a sensitive variable through randomized responses

Rao and Wu's re-scaling bootstrap modified to achieve extended coverages

Adaptive cluster sampling (ACS) due to Thompson (J Am Stat Assoc 85(412):1050–1059, 1990) is a tool to survey rare and hidden elements in a population as an improvement over traditional survey procedures. In ACS, if an observed sampled unit satisfies the given criterion of rarity, its neighboring units are added to the sample and this is continued until one is detected with no rarity. Chaudhuri (Calcutta Stat Assoc Bull 50(3–4):238–253, 2000) extended the above to unequal probability sampling as Adaptive sampling. In practice, often network sizes turn out too big demanding high cost and time. So, Chaudhuri et al. (J Stat Plan Inference 121: 175–189, 2004) gave a subsampling technique to restrict the sample size in Adaptive Sampling. Towards this end, Chaudhuri et al. (J Stat Plan Inference 134: 254–267, 2005) developed a sample size restriction technique. But in Adaptive sampling, capturing neighboring rare units turn out difficult because of various hazards. We propose to try Royall’s (Biometrika 57: 377–389, 1970) prediction approach here to model features of uncaptured network units. We employ Brewer’s (J Am Stat Assoc 74 (368): 911–915, 1979) model-assisted approach to derive a predictor with asymptotic design unbiasedness based on unequal probability samples and examine its efficacy by simulations.

A prediction approach in adaptive sampling

The finite population proportion of a sensitive characteristic is estimated indirectly by using Randomized Response (RR) Techniques (RRT’s) pioneered by Warner (1965) followed by several other RRT’...

Sanghamitra Pal

Papers

A new unrelated question randomized response model

On efficacy of empirical Bayes estimation of a finite population mean of a sensitive variable through randomized responses

Rao and Wu's re-scaling bootstrap modified to achieve extended coverages

A prediction approach in adaptive sampling

Estimation of finite population distribution function of sensitive variable