As part of a programme of landscape-scale habitat surveillance in the United Kingdom (UK), the effect of grassland sampling intensity on the outcome of numerical classification was assessed. Sample quadrats from two regions of the UK were available for post priori analysis; a random sample from Great Britain (GB), with grasslands sampled in proportion to area, and an independent stratified rand...

Khoshnevisan et al. (2007) proposed a general family of estimators for population mean using known value of some population parameters in simple random sampling. The objective of this paper is to propose a family of combined-type estimators in stratified random sampling adapting the family of estimators proposed by Khoshnevisan et al. (2007) under non-response. The properties of proposed family...

We propose some ratio-type variance estimators using ratio estimators for the population mean in literature. We obtain mean square error (MSE) equations of proposed estimators and show that proposed estimators are more efficient than the traditional ratio estimator, suggested by [C.T. Isaki, Variance estimation using auxiliary information, Journal of the American Statistical Association 78 (198...

The First Part of this paper deals with the study of the precision of an unbiased estimator of a population income inequality index in the stratified samplings with and without replacement. In this Second Part, we first analyze the asymptotic distribution of the sample income inequality index. Then, we establish different criteria to select the suitable sample size to estimate the population in...

Researchers have highlighted that the main factors that contribute to IT service failure are the people, process and technology. However, relatively few empirical studies examine to what degree these factors contribute to service disruptions in the public sector organizations. This study explores the IT service management (ITSM) at eight (8) Front-end Agencies, four (4) Ministries and six (6) D...

Science and engineering problems subject to uncertainty are frequently both computationally expensive feature nonsmooth parameter dependence, making standard Monte Carlo too slow, excluding efficient use of accelerated quantification methods relying on strict smoothness assumptions. To remedy these challenges, we propose an adaptive stratification method suitable for with significantly reduced ...