in interventional or observational longitudinal studies, the issue of missing values is one of the main concepts that should be investigated. the researcher's main concerns are the impact of missing data on the final results of the study and the appropriate methods that missing values should be handled. regarding the role and the scale of the variable that missing values have been occurred and ...

The goal of this study is to introduce a statistical method regarding the analysis of specific latent data for regression analysis of the discrete data and to build a relation between a probit regression model (related to the discrete response) and normal linear regression model (related to the latent data of continuous response). This method provides precise inferences on binary and multinomia...

a model for missing data in mixed binary and continuous responses, which can be used on cross-sectional data, is presented. in this model response indicator for the binary response can be dependent on the continuous response. a closed form for the likelihood is found. for data with a complicated pattern of missing responses some new residuals are also proposed. the model of multiplicative heter...

A model for missing data in mixed binary and continuous responses, which can be used on cross-sectional data, is presented. In this model response indicator for the binary response can be dependent on the continuous response. A closed form for the likelihood is found. For data with a complicated pattern of missing responses some new residuals are also proposed. The model of multiplicative heter...

in many industrial and non-industrial applications the quality of a process or product is characterized by a relationship between a response variable and one or more explanatory variables. this relationship is referred to as profile. in the past decade, profile monitoring has been extensively studied under the normal response variable, but it has paid a little attention to the profile with the ...

Objectives: The categorical data analysis is very important in statistics and medical sciences. When the binary response variable is misclassified, the results of fitting the model will be biased in estimating adjusted odds ratios.  The present study aimed to use a method to detect and correct misclassification error in the response variable of Type 2 Diabetes Mellitus (T2DM), applying binary ...

We examine the Rasch model for latent structure para‎- ‎meters in ‎binary and multiple response questionnaires and develop‎ ‎methodologies and data-analytic tools for assessing‎ ‎collaboration/che‎- ‎ating in multiple choice tests

this corresponds to the logistic df. The transformation may be seen to induce a certain degree of heteroscedasticity into the model. Suppose each observation p̂i is based on a moderately large sample of ni observations with p̂i → pi. We may then use the δ-method to compute the variability of logit(p̂i), V (g(p̂i)) = (g (pi)) V (p̂i) g(p) = log(p/(1− p)) g′(p) = 1− p p · d dp ( p 1− p ) = 1 p(1− p)