Hartley-Ross Type Estimators for Population Mean Using Known Parameters of Auxiliary Variate

This article proposes Hartley-Ross type unbiased estimators of finite population mean using information on known parameters of auxiliary variate when the study variate and auxiliary variate are positively correlated. The variances of the proposed unbiased estimators are obtained. It has been shown that the proposed estimators are more efficient than the simple mean estimator, usual ratio estimator and estimators proposed by Sisodia and Dwivedi (1981 Sisodia , B. V. S. , Dwivedi , V. K. ( 1981 ). A modified ratio estimator using coefficient of variation of auxiliary variable . J. Indian Soc. Agricultural Statist. 33 ( 1 ): 13 – 18 . [Google Scholar]), Kadilar and Cingi (2006 Kadilar , C. , Cingi , H. ( 2006 ). A new ratio estimator using correlation coefficient . Int. Statist. 1 – 11 . [Google Scholar]), and Kadilar et al. (2007 Kadilar , C. , Candan , M. , Cingi , H. ( 2007 ). Ratio estimators using robust regression . Hacet. J. Math. Statist. 36 ( 2 ): 181 – 188 .[Web of Science ®] , [Google Scholar]) under certain realistic conditions. Empirical studies are also carried out to demonstrate the merits of the proposed unbiased estimators over other estimators considered in this article.     

Bayesian Survival Analysis of Head and Neck Cancer Data Using Lognormal Model

The paper considers a lognormal model for the survival times and obtains a Bayes solution by means of Gibbs sampler algorithm when the priors for the parameters are vague. The formulation given in the paper is mainly focused for censored data problems though it is equally well applicable for complete data scenarios as well. For the purpose of numerical illustration, we considered two real data sets on head and neck cancer patients when they have been treated using either radiotherapy or chemotherapy followed by radiotherapy. The paper not only compares the survival functions for the two therapies assuming a lognormal model but also provides a model compatibility study based on predictive simulation results so that the choice of lognormal model can be justified for the two data sets. The ease of our analysis as compared to an earlier approach is certainly an advantage.     

Unraveling the enigma of Indira Gandhi’s rise in Indian politics: a woman leader’s quest for political legitimacy

This article employs Bourdieu??s notion of symbolic capital to explain how Indira Gandhi gained legitimacy in Indian politics. It reveals that, in spite of having belonged to the politically illustrious Nehru family, Gandhi suffered numerous indignities as a minister in the immediate post-Nehruvian period because the incumbent political elite at the time, the Syndicate, devalued the symbolic value of her family-name-based-capital of mass popularity. In the meantime, changes in the clientelistic relations between the landed and landless caste groups had created conditions for the failure of the Syndicate??s claim that their capital of popularity among politicians was the symbolic capital of the Indian political field. Aware of social changes taking place in the countryside, Gandhi took advantage of her access to the symbolic power of the state offices to classify the landless caste groups as garib (poor) in order to defeat the Syndicate electorally. Having established her capital of popularity among the masses as the symbolic capital of the Indian political field, she cemented its status by using her control over ruling party leaders?? access to state offices and simultaneously creating a new classification of a competent leader in the ruling party. This study contributes to the existing studies of leadership, especially leadership by women, and the legitimacy-gaining process by revealing the role of contest among the elite over the meaning of symbolic capital in creating or destroying their respective authority.     

Inclusive Water Governance: a Global Necessity. Lessons from India

Efficient water management has emerged as a critical challenge of environmental protection and human security in the twenty-first century. Lack of water management affects the hydrological cycle of the earth, leading to a major influence on the livelihood of the people. This paper reviews the enhanced role of IWRM in water management. It discusses the functioning of Integrated Water Resource Management (IWRM) as a global approach to overcome the challenge(s) regarding the efficient management of water. Further, the paper argues for ‘inclusive water governance’ developed at the local level as against IWRM developed at the global level. The paper emphasizes on the approach of inclusive water governance as a practical solution to address the water management crisis in India, in particular, and developing countries, in general. Inclusive water governance is sensitive to equitable access to water, as a human right, and also to sustainability of water, as an important resource.

Diagnosis of Organizational Culture in Public Sector Undertakings Undergoing Reforms

This study investigates a comparative analysis of civil servants’ perceptions of organisational culture in two public sector undertakings undergoing public sector reform in Fiji, and a public administration industry reference group. Data collected from Fijian civil servants and analysed superficially indicate similarity in cultures and suggested intervention strategies to create the preferred organisational cultures. In addition, the findings show the criticality of deciphering and understanding public sector organisational culture at both the superficial and deeper levels, in order to guide intervention strategies and support sustained organisational change.     

Assessing effect of dominant land-cover types and pattern on urban forest biomass estimated using LiDAR metrics

Accurate estimates of biomass in urban forests can help improve strategies for enhancing ecosystem services. Landscape heterogeneity, such as land-cover types and their spatial arrangements, greatly affects biomass growth, and it complicates the estimation of biomass. Application of LiDAR data is a typical approach for mapping forest biomass and carbon stocks across heterogeneous landscapes. However, little is known about how urban land uses and pattern impact biomass and estimates derived from LiDAR analysis. In this study, we examined the relationship between LiDAR-derived biomass and dominant land-cover types using field-measured estimates of aboveground forest biomass in an urbanized region of North Carolina, USA. Three objectives drove this research: 1) we examined the local effects of dominant land cover types on urban forest biomass; 2) we identified the spatial scale at which dominant land cover influences biomass estimates; 3) we investigated whether the fine-scale, spatial heterogeneity of the urban landscape contributed to forest biomass. We used multiple linear regression to relate field-measured biomass to LiDAR metrics and land cover densities derived from Landsat and LiDAR data. The biomass model developed from variables derived from LiDAR first returns produced biomass estimates similar to using all LiDAR returns. Although three land-cover types (impervious surface, managed clearings, and farmland) exhibited a negative relationship with biomass, only impervious surface was statistically significant. The biomass model that used impervious surface densities between 100 m and 175 m radial buffers produced the highest adjusted R ² with lower RMSE values. Our study suggests that impervious surface impacted forest biomass estimates considerably in urbanizing landscapes with the greatest effect between 100 and 175 m from a forest stand. Managed clearing and farmland types negatively impacted biomass estimation albeit not as strongly as impervious surface. Overall, we found that accounting for impervious surface density and its proximity to forest in biomass models may improve urban forest biomass estimates.     

Identities beyond ethnic-based subordination or conflict in the Southeast Asian borderlands: a case study of Lao villagers in northeast Cambodia

This paper explores everyday expressions of identity by Lao villagers living in northeast Cambodia near the border with Laos, and uses this to reflect on trends in studies of identity in the remote borderlands of Southeast Asia. I argue that while ethnicity is certainly crucial, discipline-specific emphases – ethnic-based subordination or conflict for social studies and political studies, respectively – result in less attention to other important dynamics shaping identity. Furthermore, I suggest that low-profile case studies not strongly linked to ethnic-based subordination or conflict – such as the Lao in Cambodia – require study for a more comprehensive view of identity in the borderlands. The case study from northeast Cambodia identifies four key dynamics shaping identity: inter-state relations, ethno-national identities, cross-border livelihoods and social status. I show how minimal inter-state tensions and varied personal connections to Laos were important in facilitating villagers’ involvement in lucrative illegal cross-border logging in Laos, but the latter were never used to justify this practice. Instead in this everyday setting, villagers emphasised their sense of marginalisation within Cambodia and thus demonstrate their regular prioritisation of status.     

Model-based study of families of exponential-type estimators in presence of non response

Among other types of non sampling errors, non response error (NRE) is an inherent component of any sample survey, which is supposed to be given much attention during the designing and execution stages. With increasing awareness of these estimators, therefore, there is an urge for the development of suitable techniques for controlling them.

This article proposes two families of estimators for population mean in the presence of non response and discuses various properties under model approach, namely polynomial regression model. The families include some existing estimators. Comparison of efficiencies along with the robustness of the estimators under misspecification of models has been empirically discussed.     

Bayesian estimation and prediction based on lognormal record values

In this paper we consider the problems of estimation and prediction when observed data from a lognormal distribution are based on lower record values and lower record values with inter-record times. We compute maximum likelihood estimates and asymptotic confidence intervals for model parameters. We also obtain Bayes estimates and the highest posterior density (HPD) intervals using noninformative and informative priors under square error and LINEX loss functions. Furthermore, for the problem of Bayesian prediction under one-sample and two-sample framework, we obtain predictive estimates and the associated predictive equal-tail and HPD intervals. Finally for illustration purpose a real data set is analyzed and simulation study is conducted to compare the methods of estimation and prediction.     

An alternative method for forecasting price volatility by combining models

In this article, we study the volatility in the monthly price series of edible oils in domestic and international markets using the two popular family of nonlinear time-series models, viz, Generalized autoregressive conditional heteroscedastic (GARCH) models and Stochastic volatility (SV) models. To improve the forecasts of the volatility process, we also propose a new method of combining the volatility of these two competing models using the powerful technique of Kalman filter. The individual models as well as the combined models are assessed on their ability to predict the correct directional change (CDC) in future values as well as other goodness-of-fit statistics. Further, forecasting performance are also evaluated by computing various measures to validate the proposed methodology.