روشي براي برآورد پارامترهاي مدل رگرسيون لجستيك با وجود مقادير گمشده در متغيير كمكي و كاربرد آن در بررسي بيماري گواتر

Objective: In large -- scale sampling, we are always facing non-responses item(s) non-response unit(s) or both. in fitting a model to the data we have two groups of variables, namely dependent and independent variables. Non-response may occur for each of these groups of variables. Materials and Method: in this paper we assume that Y as a categorical dependent variable, Z and X as independent variables and we assume that missing observations on independent variables. The first two variables are fully observed and we assume that the mechanism of missing-ness is random (MAR). Results: In order to estimate parameters, a model is devised based on likelihood function for the whole data set including missing data and the estimation of parameters are compared with those obtained by statistical software such as S-Plus which are only based on complete observed data and ignore missing units. Conclusion: Our results show that the estimations obtained using maximum likelihood based model is superior to the standard estimations for the approach utilized by the soft wares. The comparison is made on a set of health survey data on goiter disease carried out in Ghazvin province.