In spite of being a common method for estimating the model parameters, Maximum Likelihood (ML) may give bias results small sample sizes. To overcome this problem, Bayesian is usually utilized to obtain estimates parameters as an alternative ML method. study, real data set was analyzed by using binary logistic regression model. Parameters were estimated and methods. Modeling performance logistic...

1. The Effect of Response Level Ordering on Parameter Estimate Interpretation 2. Odds Ratios 2.1 Binary Explanatory Variable Modeling the Event 2.2 Binary Explanatory Variable Modeling the Nonevent 2.3 Continuous Explanatory Variable 3. Predicted Probabilities 4. Predicted by Observed Classification Tables 4.1 Classification Using Predicted Probabilities 4.2 Classification Using Bias-adjusted P...

Unobserved confounding is a well known threat to causal inference in non-experimental studies. The instrumental variable design can under certain conditions be used to recover an unbiased estimator of a treatment effect even if unobserved confounding cannot be ruled out with certainty. For continuous outcomes, two stage least squares is the most common instrumental variable estimator used in ep...

Unobserved confounding is a well-known threat to causal inference in non-experimental studies. The instrumental variable design can under certain conditions be used to recover an unbiased estimator of a treatment effect even if unobserved confounding cannot be ruled out with certainty. For continuous outcomes, two stage least squares is the most common instrumental variable estimator used in ep...

We investigate a generalized linear model for dimensionality reduction of binary data. The model is related to principal component analysis (PCA) in the same way that logistic regression is related to linear regression. Thus we refer to the model as logistic PCA. In this paper, we derive an alternating least squares method to estimate the basis vectors and generalized linear coefficients of the...

BACKGROUND A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence). Scoring systems based on binary logistic regression models are a specific type of predictive model. OBJE...

‎A transition binary logistic model with random coefficients is‎ ‎proposed to model the unemployment statues of household members in‎ ‎two seasons of spring and summer‎. ‎Data correspond to the labor‎ ‎force survey performed by Statistical Center of Iran in 2006.‎ ‎This model is introduced to take into account two kinds of‎ ‎correlation in the data one due to the longitudinal nature o...