Exact Likelihood Inference for Two Exponential Populations Under Joint Progressive Type-II Censoring

Comparative lifetime experiments are of great importance when the interest is in ascertaining the relative merits of two competing products with regard to their reliability. In this article, we consider two exponential populations and when joint progressive Type-II censoring is implemented on the two samples. We then derive the moment generating functions and the exact distributions of the maximum likelihood estimators (MLEs) of the mean lifetimes of the two exponential populations under such a joint progressive Type-II censoring. We then discuss the exact lower confidence bounds, exact confidence intervals, and simultaneous confidence regions. Next, we discuss the corresponding approximate results based on the asymptotic normality of the MLEs as well as those based on the Bayesian method. All these confidence intervals and regions are then compared by means of Monte Carlo simulations with those obtained from bootstrap methods. Finally, an illustrative example is presented in order to illustrate all the methods of inference discussed here.     

DISPERSION CONTROL FOR MULTIVARIATE PROCESSES

This paper presents some techniques for monitoring and controlling the dispersion of multivariate normal processes based on subgroup data. The procedures involve use of independent statistics resulting from the decomposition of the covariance matrix. Those that do not depend on prior estimates of the process covariance matrix are particularly attractive to short-run or low volume manufacturing environments.     

Inference on P(Y < X) in Bivariate Rayleigh Distribution

This paper deals with the estimation of reliability R = P(Y < X) when X is a random strength of a component subjected to a random stress Y, and (X, Y) follows a bivariate Rayleigh distribution. The maximum likelihood estimator of R and its asymptotic distribution are obtained. An asymptotic confidence interval of R is constructed using the asymptotic distribution. Also, two confidence intervals are proposed based on Bootstrap method and a computational approach. Testing of the reliability based on asymptotic distribution of R is discussed. Simulation study to investigate performance of the confidence intervals and tests has been carried out. Also, a numerical example is given to illustrate the proposed approaches.     

Comparing different propensity score estimation methods for estimating the marginal causal effect through standardization to propensity scores

Hernan and Robins proposed a method for calculating marginal causal effect of treatment using standardization to propensity scores.

?Data adaptive methods have been suggested as alternatives to logistic regression for the estimation of propensity scores. We examined the performance of various data mining methods using simulated data. The estimators' performance was evaluated in terms of relative bias, 95% CI coverage rate, and mean squared error.

?All methods (except CART and GBM) displayed generally acceptable performance. However, under the conditions of moderate non-additivity and moderate nonlinearity, ANN and SL outperformed logistic regression with better bias reduction and more consistent 95% CI coverage.     

Urban Growth and Housing Quality in Iran

Rapid urban population growth and the lack of effective policies in order to desired population settlement, has encountered Iran’s major cities with multiple social, economic and environment problems. Physical expansion of cities in order to settlement the increasing population, causes the formation of new textures surrounding old urban textures. In the new textures, urban housing is formed with the modern approach and least logical connection with common practice in the old textures. Therefore, the main objective of this paper is to evaluate the housing quality and identify strengths and weaknesses in old and new urban textures to improve the quality of urban housing in Iran. Household survey were conducted to collect the needed data in the old and new textures in Khorramabad city. Results showed that the satisfaction level of housing quality in new texture is far more than old ones. The highest satisfaction level in both textures are related to housing basic facilities domain. However, satisfaction level of this domain in new texture is much greater compared to the old texture. The lowest level of satisfaction is related to the quality of neighborhood domain in old texture and to the access to public services domain in the new one. Results from the factor analysis to identify the dimensions of housing quality in old and new textures illustrated that housing quality is a multidimensional concept, studying of which requires considering all dimensions and aspects.     

A new method for generating distributions with an application to exponential distribution

A new method has been proposed to introduce an extra parameter to a family of distributions for more flexibility. A special case has been considered in detail, namely one-parameter exponential distribution. Various properties of the proposed distribution, including explicit expressions for the moments, quantiles, mode, moment-generating function, mean residual lifetime, stochastic orders, order statistics, and expression of the entropies, are derived. The maximum likelihood estimators of unknown parameters cannot be obtained in explicit forms, and they have to be obtained by solving non linear equations only. Further, we consider an extension of the two-parameter exponential distribution also, mainly for data analysis purposes. Two datasets have been analyzed to show how the proposed models work in practice.     

Why Do Neighbors Have Different Environmental Priorities? Analysis of Environmental Risk Perception in a Beirut Neighborhood

Differences in environmental priorities within an urban neighborhood of Beirut are analyzed. The explanatory capabilities of five categories of contextual variables are compared: socioeconomic status, locality, health, behavior, and environmental beliefs. Semi-structured interviews with key individuals in the community and residents were first conducted. Four environmental issues of concern were identified. A survey was carried out to identify the relative priority accorded by respondents to these four issues, and to measure variables likely to explain differences of opinion. Bivariate and multivariate logistic regression analyses were conducted for each of the four problems. The 99% confidence interval (CI) of the odds ratio (OR) was used as a test of significance. Respondents suffering from a respiratory disease (OR = 6.94, 99%CI = 1.54-31.25), those living in less crowded houses (OR = 4.88, 99%CI = 1.38-17.24), and those not living close to the neighborhood's industrial street (OR=5.26, 99%CI = 1.01-27.78) are significantly more likely to rank poor air quality first. Significant associations are found between poor water quality as first priority and nonpresence of a smoker in the household (OR = 6.12, 99%CI = 1.84-20.32) and perception of water salinity as a problem (OR = 7.46, 99%CI = 1.50-37.03). Males (OR = 6.94, 99%CI = 1.02-47.62) and tenants versus owners (OR = 10.49, 99%CI = 1.36-80.61) are significantly more likely to rank the residential-industrial mix first. Socioeconomic variables retain their explanatory capability in the studied neighborhood, despite relatively small income disparities. Behavioral variables, such as smoking, may be causative factors of priorities. Analyzing relative priorities, rather than "concern" or lack of it, reveals more complex patterns of association. Identifying environmental-perception divide lines can help develop a more inclusive and effective participatory environmental management.