A semiparametric approach for analyzing nonignorable missing data
نویسندگان
چکیده
منابع مشابه
A Semiparametric Approach for Analyzing Nonignorable Missing Data
In missing data analysis, there is often a need to assess the sensitivity of key inferences to departures from untestable assumptions regarding the missing data process. Such sensitivity analysis often requires specifying a missing data model that commonly assumes parametric functional forms for the predictors of missingness. In this paper, we relax the parametric assumption and investigate the...
متن کاملa 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 ...
15 صفحه اولA semi-parametric approach to fractional imputation for nonignorable missing data
Parameter estimation with nonignorable missing data is a challenging problem in statistics. Fully parametric approach for joint modeling of the response model and the population model can produce results that are very sensitive against the failure of the assumed model. We consider a more robust approach of modeling by describing the model for the nonresponding part as a exponential tilting of t...
متن کاملBayesian quantile regression for longitudinal studies with nonignorable missing data.
We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the within-subject correlation by introduc...
متن کاملParametric fractional imputation for mixed models with nonignorable missing data
Inference in the presence of non-ignorable missing data is a widely encountered and difficult problem in statistics. Imputation is often used to facilitate parameter estimation, which allows one to use the complete sample estimators on the imputed data set. We develop a parametric fractional imputation (PFI) method proposed by Kim (2011), which simplifies the computation associated with the EM ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistica Sinica
سال: 2011
ISSN: 1017-0405
DOI: 10.5705/ss.2009.252