Asymptotic design-cum-model based estimation of variances of estimated linear regression coefficients in survey sampling with unequal probabilities

Postulating a super-population linear regression model for a variable of interest on an auxiliary variable we consider design-based estimation of regression coefficients on drawing a sample with unequal probabilities from a survey population. Asymptotic design-cum-model based variance estimation procedures are proposed.     

Model-cum-design-based estimation of the prevalence rate of a disease in a locality using spatial smoothing

In this article, we study the problem of estimating the prevalence rate of a disease in a geographical area, based on data collected from a sample of locations within this area. If there are several locations with zero incidence of the disease, the usual estimators are not suitable and so we develop a new estimator, together with an unbiased estimator of its variance, which may be appropriately used in such situations. An application of this estimator is illustrated with data from a large-scale survey, which was carried out in the city of Kolkata, India, to estimate the prevalence rate of stroke. We show that spatial modelling may be used to smooth the observed data before applying our proposed estimator. Our computations show that this smoothing helps to reduce the coefficient of variation and such a model-cum-design-based procedure is useful for estimating the prevalence rate. This method may of course be used in other similar situations.     

Variance estimation in model assisted survey sampling

Two versions of Yates-Grundy type variance estimators are usually employed for large samples when estimating a survey population total by a generalized regression (Greg, in brief) predictor motivated by consideration of a linear regression model. Their two alternative modifications are developed so that the limiting values of the design expectations of the model expectations of variance estimators 'match' respectively the (I) model expectations of the Taylor approximation of the design variance of the Greg predictor and the (II) limiting value of the design expectation of the model expectation of the squared difference between the Greg predictor and the population total. The exercise is extended to yield modifications needed when randomized response (RR) is only available rather than direct response (DR) when one encounters sensitive issues demanding protection of privacy. A comparative study based on simulation is presented for illustration..     

On the efficiency of a testimator for the weibull shape parameter

In estimating the shape parameter of a two-parameter Weibull distribution from a failure-censored sample, a recently popular procedure is to employ a testimator which is a shrinkage estimator based on a preliminary hypothesis test for a guessed value of the parameter. Such an adaptive testimator is a linear compound of the guessed value and a statistic. A new compounding coefficient is numerically shown to yield higher efficiency in many situations compared to some of the existing ones.     

The role of reversals in order‐restricted inference

A statistical model is said to be an order‐restricted statistical model when its parameter takes its values in a closed convex cone C of the Euclidean space. In recent years, order‐restricted likelihood ratio tests and maximum likelihood estimators have been criticized on the grounds that they may violate a cone order monotonicity (COM) property, and hence reverse the cone order induced by C. The authors argue here that these reversals occur only in the case that C is an obtuse cone, and that in this case COM is an inappropriate requirement for likelihood‐based estimates and tests. They conclude that these procedures thus remain perfectly reasonable procedures for order‐restricted inference.     

Variance estimation with randomized response

Presenting a general procedure of eliciting a randomized response (RR) from selected persons in order to estimate the total of a sensitive variable related to a finite survey population, we consider two estimators along with variance estimators treating the case of sampling with probabilities proportional to (known) size measures (PPS) with replacement (WR), drawing analogies with multi-stage sampling and note their relative efficacies.     

The relationship between training outsourcing and employee commitment to organization

In many countries, the human resource practice of training outsourcing has emerged as one of the fastest growing segments of the broader business process outsourcing industry. In spite of the growing popularity in professional practice, training outsourcing continues to be subjected to critical review and ongoing debate with most attention focused on the decision to ‘outsource’ or ‘not to outsource’. However, there exists a shortage of research on training outsourcing as a human resource development (HRD) practice and the potential relationships with desired organizational outcomes including employee commitment. This exploratory international study extends previous research that has examined the relationship between training and organizational commitment by focusing exclusively on outsourced training. Data were collected from information technology firms in two countries: India and the United States. Results showed positive relationships between specific measures of employee perceptions of quality, usefulness and supervisor support for outsourced training with organizational commitment. Recommendations are made for future research as well as for professional practice to guide HRD practitioners involved in the rapidly growing global practice of training outsourcing.     

A comparison of robust estimators based on two types of trimming

The least trimmed squares (LTS) estimator and the trimmed mean (TM) are two well-known trimming-based estimators of the location parameter. Both estimates are used in practice, and they are implemented in standard statistical software (e.g., S-PLUS, R, Matlab, SAS). The breakdown point of each of these estimators increases as the trimming proportion increases, while the efficiency decreases. Here we have shown that for a wide range of distributions with exponential and polynomial tails, TM is asymptotically more efficient than LTS as an estimator of the location parameter, when they have equal breakdown points.