A robust estimation method for the linear regression model parameters with correlated error terms and outliers

Independence of error terms in a linear regression model, often not established. So a linear regression model with correlated error terms appears in many applications. According to the earlier studies, this kind of error terms, basically can affect the robustness of the linear regression model analysis. It is also shown that the robustness of the parameters estimators of a linear regression model can stay using the M-estimator. But considering that, it acquires this feature as the result of establishment of its efficiency. Whereas, it has been shown that the minimum Matusita distance estimators, has both features robustness and efficiency at the same time. On the other hand, because the Cochrane and Orcutt adjusted least squares estimators are not affected by the dependence of the error terms, so they are efficient estimators. Here we are using of a non-parametric kernel density estimation method, to give a new method of obtaining the minimum Matusita distance estimators for the linear regression model with correlated error terms in the presence of outliers. Also, simulation and real data study both are done for the introduced estimation method. In each case, the proposed method represents lower biases and mean squared errors than the other two methods.KEYWORDS: Robust estimation method, minimum Matusita distance estimation method, non-parametric kernel density estimation method, correlated error terms, outliers     

Selective Maintenance in System Reliability with Random Costs of Repairing and Replacing the Components

In this paper we consider the problem of determining the optimum number of repairable and replaceable components to maximize a system's reliability when both, the cost of repairing the components and the cost of replacement of components by new ones, are random. We formulate it as a problem of non-linear stochastic programming. The solution is obtained through Chance Constrained programming. We also consider the problem of finding the optimal maintenance cost for a given reliability requirement of the system. The solution is then obtained by using Modified E-model. A numerical example is solved for both the formulations.     

Distribution of linear combination of exponential variates

The exact distribution of a linear combination of n independent negative exponential random variables, when the coefficients of the linear combination are distinct and positive quantities, is well-known. This paper extends the above result to the general case, namely when the coefficients are arbitrary real numbers, positive or negative, distinct or coincident.     

Sexual orientation and full-time monthly earnings, by public and private sector: evidence from Swedish register data

In this paper we explore annual earnings as well as full-time monthly earnings differentials resulting from sexual orientation. We observe that gay males are at an earnings disadvantage compared to male heterosexuals regardless of which earnings measure we use. This earnings disadvantage is found to be larger when we compare gay and heterosexual males who are working full-time. In addition, the disadvantage is larger in the private than in the public sector. Lesbians, however, earn more than heterosexual females. This earnings advantage is considerably smaller when we study full-time monthly rather than annual earnings but an earnings advantage for lesbians at the top of the earnings distribution is documented regardless of which earnings measure we use. In addition, lesbians are doing better than female heterosexuals in the public sector. To sum up, the results indicate that gay males face obstacles on the labor market that hinder them from reaching top-level positions and high earnings. The earnings advantage observed for lesbians is likely to stem from the fact that lesbians devote more time to market work than heterosexual females do.     

Estimation of the mean of the exponential distribution using moving extremes ranked set sampling

Moving Extremes Ranked Set Sampling (MERSS) is a useful modification of Ranked Set Sampling (RSS). Unlike RSS, MERSS allows for an increase of set size without introducing too much ranking error. The method is considered parametrically under exponential distribution. Maximum likelihood estimator (MLE), and a modified MLE are considered and their properties are studied. The method is studied under both perfect and imperfect ranking (with error in ranking). It appears that these estimators can be real competitors to the MLE using the usual simple random sampling (SRS).     

Ascertaining the effect of planned organizational change on the work environment

In a dynamic environment, organizations often are required to effect major changes in operations. The success of such changes invariably depends upon numerous factors one of which is how the change affects the work environment. Therefore, in planning organizational change it is important to consider the work environment and to objectively evaluate changes in the work environment in a timely fashion so that corrective actions may be formulated if needed. Such framing and monitoring of the organizational change facilitates successful implementation of the organizational change. This paper suggests a measure which may be used in evaluating an organization's work environment. A case is discussed.     

A multivariate version of Gini's rank association coefficient

In this paper, we introduce a multivariate generalization of the population version of Gini's rank association coefficient, giving a response to this open question posed in [4]. We also study some properties of this version, present the corresponding results for the sample statistic, and provide several examples.     

Normalité asymptotique d'estimateurs convergents du mode conditionnel

We state sufficient conditions for asymptotic normality of convergent estimates of the conditional mode, irrespective of data dependence, and give an application to α-mixing stationary processes.