This article discusses some properties of the rst order regression method for imputation of missing values on an explanatory variable in linear regression model and presents an estimation strategy based on hy pothesis testing

Statistical process control methods for monitoring processes with univariate ormultivariate measurements are used widely when the quality variables fit to known probabilitydistributions. Some processes, however, are better characterized by a profile or a function of qualityvariables. For each profile, it is assumed that a collection of data on the response variable along withthe values of the c...

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This article discusses some properties of the rst order regression method for imputation of missing values on an explanatory variable in linear regression model and presents an estimation strategy based on hypothesis testing.

this paper compares the value relevance of book value and dividends versus book value and reported earnings. the main purpose of this study explains which of dividends, book value and earnings are more value relevance. thus, we have examined 98 firms of tehran stock exchange over a period 1377-1382. our methodology of examining the information content of various income statement and balance she...

This paper proposes a new class of nonlinear time series models in which one of the coefficients of an existing regression model is frequency dependent – that is, the relationship between the dependent variable and this explanatory variable varies across its frequency components. We show that such frequency dependence implies that the relationship between the dependent variable and this explana...

in many applications the quality of a process or product is best characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. profile monitoring is used to understand and to check the stability of this relationship over time. therefore checking the capability of a profile seems essential. process capability indices have become very...