in this study, an effort has been made to determine the most important risk factors of tuberculosis (tb) in district mardan. a total of 645 cases were examined, and their personal and medical data were collected. for each case, the phenomenon of tb was studied in relation to different risk factors. statistical techniques of logistic regression and backward elimination procedure were used to ana...

The present study aims to model systematic risk using financial and accounting variables. Accordingly, the data for 174 companies in Tehran Stock Exchange are extracted for the period of 2006 to 2016. First, the systematic risk index is estimated using the ARFIMA-FIGARCH model. Then, based on the research background, 35 affective financial and accounting variables are simultaneously used with t...

gression enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome or response can be either dichotomous (yes, no) or ordinal (low, medium, high). During dichotomous response, we are performing standard logistic regression and for ordinal response, model that uses standard logistic regression formula with feature selection using forw...

Statistical models are simple mathematical rules derived from empirical data describing the association between an outcome and several explanatory variables. In a typical modeling situation statistical analysis often involves a large number of potential explanatory variables and frequently only partial subject-matter knowledge is available. Therefore, selecting the most suitable variables for a...

This study intends to identify the determinants of corporate dividend policy followed in Bangladesh. The uses a firm-level panel data set 61 companies from eight major sectors DSE for ten years 2008 2017. Pearson correlation coefficient and backward elimination method multiple regression have been used find out results study. shows mixed relationship between per share ownership structure, reser...

Two sequential methods are described for sampling constrained binary sequences from partial solutions. The backward method computes elimination ideals over finite fields and constructs partial solutions that extend. The forward method uses numerical global optimization to determine which partial solutions extend. The methods are applied to restricted orderings, binary dynamics, and random graphs.

This paper proposes a feature selection method that combines various feature selection techniques. Feature selection has been realized as one of the most important processes in various applications, especially pattern classification problems. When too many attributes are involved, training a machine to classify patterns into their respective classes is seemingly impossible. Hence, selecting goo...

We consider the problem of selecting grouped variables (factors) for accurate prediction in regression. Such a problem arises naturally in many practical situations with the multi-factor ANOVA problem as the most important and well known example. Instead of selecting factors by stepwise backward elimination, we focus on estimation accuracy and consider extensions of the LASSO, the LARS, and the...