Rare and clustered population estimation using the adaptive cluster sampling with some robust measures

The use of robust measures helps to increase the precision of the estimators, especially for the estimation of extremely skewed distributions. In this article, a generalized ratio estimator is proposed by using some robust measures with single auxiliary variable under the adaptive cluster sampling (ACS) design. We have incorporated tri-mean (TM), mid-range (MR) and Hodges-Lehman (HL) of the auxiliary variable as robust measures together with some conventional measures. The expressions of bias and mean square error (MSE) of the proposed generalized ratio estimator are derived. Two types of numerical study have been conducted using artificial clustered population and real data application to examine the performance of the proposed estimator over the usual mean per unit estimator under simple random sampling (SRS). Related results of the simulation study show that the proposed estimators provide better estimation results on both real and artificial population over the competing estimators.     

Generalized estimator of population mean by using conventional and non-conventional measures in the presence of measurement errors

In survey research, it is assumed that reported response by the individual is correct. However, given the issues of prestige bias, self-respect, respondent's reported data often produces estimated values which are highly deviated from the true values. This causes measurement error (ME) to be present in the sample estimates. In this article, the estimation of population mean in the presence of measurement error using information on a single auxiliary variable is studied. A generalized estimator of population mean is proposed. The class of estimators is obtained by using some conventional and non-conventional measures. Simulation and numerical study is also conducted to assess the performance of estimators in the presence and absence of measurement error.     

Analysis of microtubule dynamics using growth curve models

Microtubules are part of the structural network within a cell's cytoplasm, providing structural support as well as taking part in many of the cellular processes. A large body of data provide evidence that dynamics of microtubules in a cell is reponsible for the performance of many critical cellular functions such as cell division. In this article, we study the effect of four different isoforms of a protein tau on microtubule dynamics using growth curve models. The results show that a linear growth curve model is sufficient to explain the data. Moreover, we find that a mutated version of a 3-repeat tau protein has a similar effect as a 4-repeat tau protein on microtubule dynamics. The latter findings conform with the biological understanding of the effect of the protein tau on microtubule dynamics.     

Intra-ethnic fissures in ethnic movements: the rise of Mohajir identity politics in post-1971 Pakistan

The article charts the rise of Mohajir identity politics in Pakistan since 1971. Most accounts of Mohajir identity politics have focused mainly on one actor, that is, the Mohajir (now Muttahida) Qaumi Movement and their politics and secondly, have remained silent on intra-ethnic fissures within the Mohajir community. The present article builds on and criticises such accounts for their partiality and presents a more nuanced understanding of politics within the Mohajir community. On the theoretical plane, it criticises modernist and primordialist accounts of ethnicity and nationalism, both of which portray a homogenous picture of ethnic groups as based on unity and shared aims. It is shown that unity and shared aims are at best, a chimera, and that intra-ethnic fissure is as important to understand as is inter-ethnic conflict when it comes to analysing ethnic movements.     

Solar energy in electric utility planning: A linear programming approach

This paper considers a multi-year capacity expansion plan for an electric utility with the option of investing in solar generation units. It is demonstrated how issues such as randomness on both the demand and supply side of the problem, as well as the varying availability of solar energy may be conveniently modelled under some plausible assumptions, to yield a large-scale linear programming problem. A brief discussion is provided relating to the evaluation of the capacity credit attributable to the solar generation units. Computational considerations, some possible simplifications, and an illustrative example are also presented.     

Consolidation and fragmentation in environmental scanning: A review and research agenda

Environmental scanning is a broadly defined concept, having first received attention from scholars in the late 1960s. Over the years a number of similar and overlapping constructs have emerged in management literature. The aim of this study, via a systematic review and thematic analysis of relevant empirical research, is to consolidate foundation environmental scanning knowledge, demonstrate how scanning research has developed and fragmented over time, and propose an agenda for future research. The first contribution of our review is a new typology of environmental scanning research made up of five discrete research views, which provides a more comprehensive and contemporary overview of the field than previous studies. The second is a proposed agenda for future research that explicitly acknowledges the role of technology, an area that is presently underdeveloped in foundation scanning literature. The third contribution is to signpost future directions for research on scanning and organisational performance using a number of theoretical perspectives. The overall outcome of our review is to move scanning research on from increasingly incremental contributions concerned with context to a place where the changing role of technology and the mechanisms through which environmental scanning contributes to competitive advantage can be more thoroughly understood.     

Estimation of finite population mean in stratified sampling using scrambled responses in the presence of measurement errors

This study focuses on the estimation of population mean of a sensitive variable in stratified random sampling based on randomized response technique (RRT) when the observations are contaminated by measurement errors (ME). A generalized estimator of population mean is proposed by using additively scrambled responses for the sensitive variable. The expressions for the bias and mean square error (MSE) of the proposed estimator are derived. The performance of the proposed estimator is evaluated both theoretically and empirically. Results are also applied to a real data set.     

BAYESIAN TEST OF HYPOTHESIS IN THE MULTINOMIAL DISTRIBUTION

For Dirichlet priors in a multinomial distribution the Bayesian test of a simple hypothesis parallels the Bartlett test of homogeneity of variances in normal samples. This parallelism enables a correction factor to be developed for a particular test statistic along the lines of Bartlett's correction. The performance of the corrected test statistic is studied numerically.