Marginal or conditional regression models for correlated non‐normal data?

Marginal Regression Models with VaryingCoeÆ ients for Correlated Ordinal

A Note On Marginal Linear Regression With Correlated Response Data

Correlated response data often arise in longitudinal and familial studies The marginal regression model and its associated generalized estimating equation GEE method are becoming more and more popular in handling such data Pepe and Anderson pointed out that there is an important yet implicit assumption behind the marginal model and GEE If the assumption is violated and a non diagonal working co...

a new approach to credibility premium for zero-inflated poisson models for panel data

هدف اصلی از این تحقیق به دست آوردن و مقایسه حق بیمه باورمندی در مدل های شمارشی گزارش نشده برای داده های طولی می باشد. در این تحقیق حق بیمه های پبش گویی بر اساس توابع ضرر مربع خطا و نمایی محاسبه شده و با هم مقایسه می شود. تمایل به گرفتن پاداش و جایزه یکی از دلایل مهم برای گزارش ندادن تصادفات می باشد و افراد برای استفاده از تخفیف اغلب از گزارش تصادفات با هزینه پائین خودداری می کنند، در این تحقیق ...

Regression models for high-dimensional data with correlated errors

where y is the n× 1 response vector; X is an n× p model matrix representing the predictors; and β is a p × 1 vector of coefficients to estimate. For mathematical simplicity, it is typical to set the first predictor as the intercept β0 so that the first column of X is the n×1 vector of ones. The intercept acts as a sink for the mean effect of included predictors, so one could remove the intercep...

Logistic Regression Models for the Analysis of Correlated Data

Up to this point in the text we have considered the use of the logistic regression model in settings where we observe a single dichotomous response for a sample of statistically independent subjects. However, there are settings where the assumption of independence of responses may not hold for a variety of reasons. For example, consider a study of asthma in children in which subjects are interv...