Edge Estimation in the Population of a Binary Tree Using Node-Sampling

Suppose a finite population of several vertices, each connected to single or multiple edges. This constitutes a structure of graphical population of vertices and edges. As a special case, the graphical population like a binary tree having only two child vertices associated to parent vertex is taken into consideration. The entire binary tree is divided into two sub-graphs such as a group of left-nodes and a group of right-nodes. This paper takes into account a mixture of graph structured and population sampling theory together and presents a methodology for mean-edge-length estimation of left sub-graph using right edge sub-graph as an auxiliary source of information. A node-sampling procedure is developed for this purpose and a class of estimators is proposed containing several good estimators. Mathematical conditions for minimum bias and optimum mean squared error of the class are derived and theoretical results are numerically supported with a test of 99% confidence intervals. It is shown that suggested class has a sub-class of optimum estimators, and sample-based estimates are closer to the true value of the population parameter.     

Estimation of Bowley's Coefficient of Skewness in the Presence of Auxiliary Information

In this paper, we suggest regression-type estimators for estimating the Bowley's coefficient of skewness using auxiliary information. To the first degree of approximation, the bias and mean-squared error expressions of the regression-type estimators are obtained, and the regions under which these estimators are more efficient than the conventional estimator are also determined. Further, a general class of estimators of the Bowley's coefficient of skewness is defined along with its properties. A class of estimators based on estimated optimum values is also defined. It is shown to the first degree of approximations that the variance of the class of estimators based on estimated optimum values is the same as that of the minimum variance of the proposed class of estimators. A simulation study is carried out to demonstrate the performance of the proposed difference estimator over the usual estimator.     

An Efficient Class of Estimators for the Population Mean Using Auxiliary Information in Stratified Random Sampling

This article addresses the problem of estimating the population mean in stratified random sampling using the information of an auxiliary variable. A class of estimators for population mean is defined with its properties under large sample approximation. In particular, various classes of estimators are identified as particular member of the suggested class. It has been shown that the proposed class of estimators is better than usual unbiased estimator, usual combined ratio estimator, usual product estimator, usual regression estimator and Koyuncu and Kadilar (2009 Koyuncu, N., Kadilar, C. (2009). Ratio and product estimators in stratified random sampling. J. Statist. Plan. Infere. 139:2552–2558.[Crossref], [Web of Science ®] , [Google Scholar]) class of estimators. The results have been illustrated through an empirical study.     

Hybrid-based confidence intervals for the ratio of two treatment means in the over-dispersed Poisson data

In many clinical trials and epidemiological studies, comparing the mean count response of an exposed group to a control group is often of interest. This type of data is often over-dispersed with respect to Poisson variation, and previous studies usually compared groups using confidence intervals (CIs) of the difference between the two means. However, in some situations, especially when the means are small, interval estimation of the mean ratio (MR) is preferable. Moreover, Cox and Lewis [4 D.R. Cox and P.A.W. Lewis, The Statistical Analysis of Series of Events, Methuen, London, 1966.[Crossref] , [Google Scholar]] pointed out many other situations where the MR is more relevant than the difference of means. In this paper, we consider CI construction for the ratio of means between two treatments for over-dispersed Poisson data. We develop several CIs for the situation by hybridizing two separate CIs for two individual means. Extensive simulations show that all hybrid-based CIs perform reasonably well in terms of coverage. However, the CIs based on the delta method using the logarithmic transformation perform better than other intervals in the sense that they have slightly shorter interval lengths and show better balance of tail errors. These proposed CIs are illustrated with three real data examples.     

Some estimation procedures of sensitive character using scrambled response techniques in successive sampling

This paper is an attempt to analyze the effect of scrambled response techniques to estimate the current population mean in two-occasion successive sampling when study and auxiliary variables are sensitive in nature. Using additive and multiplicative scrambled response models on both occasions, some generalized estimation strategies of current population mean have been proposed and their properties are examined. Empirical studies are performed on real data (abortion rate in the states of United States) and simulation studies are also carried out to evaluate the performances of the proposed estimators over other estimators. Results have proved the worthiness of the scrambled response techniques in real-life situations.     

Preparing for ICT enhanced practice learning opportunities in 2010—a speculative view

This paper presents a speculative view of the roles that a practice teacher, student and university may undertake during a practice learning opportunity in the year 2010, based upon predicted advances in information and communication technology (ICT). The article explores the likelihood of such a scenario becoming a reality by examining very briefly current developments in the area of wireless mobile devices and infrastructure. The paper presents an argument drawing on the author’s own experiences that passive acceptance or ignorance through apprehension of ICT developments should not prevent the examination and planning of how this medium may be utilised in the arena of social work in the future. This paper acts as a scenario‐planning device, presenting for discussion some of the issues that may need to be explored if ICT is to become positively integrated into the field of social work education.     

The Proximate Determinants of Fertility: Sub-national Variations

In this paper changes in the relative importance of the proximate determinants of fertility, as modernization increases, are analysed Educational attainment and type of place of current residence are used as indicators of modernization. We concentrate on the three most important proximate variables: marriage, contraception and breastfeeding, and the analysis is performed on 29 World Fertility Survey countries. Bongaarts's multiplicative model is used for the analysis but the primary data tapes make it possible to construct more refined estimates of the three indices than is usually possible. The patterns of the indices among the two sets of socio-economic sub-groups are considered, as well as the interrelationships of the indices. Fertility differences among the sub-groups are also decomposed to assess the contribution of the separate proximate determinants to sub-group variations in fertility.     

Determinants of Income Generation and Financial Performance Through Self-Help Groups

ABSTRACT

This study examines the efficacy of a microcredit-linked self-help group (SHG) program in identifying the problems faced by group members such as income generation and financial performance. To examine this, 120 members in each of three selected blocks in Birbhum District in West Bengal, India, were invited to participate. A multiple regression equation focused on identifying the contributing factors toward explaining SHG income. Results indicated that income generation for all the blocks together was significantly influenced by factors like loan amount, amount of saving, years of existence of SHG, education level of the group leader, and availability of the training facility. However, SHG-wise efficiency scores varied across the blocks that might be related to different sociocultural dimensions. Implications of the analytical findings for future research are discussed at the end of the article.     

Prevalence of job strain among Indian foundry shop floor workers

Global competition in manufacturing sector demand higher productivity levels. In this context, workers in this sector are set with high output targets, leading to job strain. In addition to the strain, hazardous conditions also prevail in some of the manufacturing processes like foundry activities. This paper attempts to appraise the prevalence of job strain among foundry shop floor workers in India with the help of Demands-Control model [8]. In this study, data was collected through a survey using 49-item Job Content Questionnaire (JCQ) [9], a widely used and well-validated test for job strain. Then the data was subjected to statistical analysis after ascertaining the reliability. This survey has revealed that 25% of workers in foundry were experiencing high job strain. Hazardous working conditions, limited decision making authority, etc. appear to be the main contributing factors for the higher levels of strain.     

Maximum likelihood estimates for the parameters of a continuous time model for first conception

The duration of time between two successive births or between marriage and first birth is an indicator of the level of fertility of a couple. Potter and Parker (1964) and Singh (1961, 1967) have suggested the Type I Geometric as a distribution appropriate for representing the length of interval to first conception leading to a live birth. Potter and Parker estimated the parameters of this distribution with the help of the first two moments. Majumdar and Sheps (1970) pointed out the limitations of these moment estimates and gave a method to obtain maximum likelihood estimates, based on formulas which are too involved for solution without the help of a computer. Singh proposed a continuous probability distribution based on another set of assumptions for the above situation. He outlined a method to obtain best asymptotically normal estimates of the parameters. These estimates are obtained after several iterations starting from any set of consistent estimates. The objective of this paper is to show that it is relatively easier to obtain maximum likelihood estimates of the parameters of the continuous model, which describes the data on duration to first conception as well as does the discrete model. Simple expressions for the moment and maximum likelihood estimates with the corresponding covariance matrices are obtained. Application is made to three sets of data.