The E-MS Algorithm: Model Selection With Incomplete Data
نویسندگان
چکیده
منابع مشابه
The E-MS Algorithm: Model Selection with Incomplete Data.
We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model, and thus allows the model to be part of the E-M iterations. We develop the procedure, known as the E-MS algorithm, under the assumption that the class of candidate models...
متن کاملModel selection in incomplete data
Model selection in complete data is a common task for the applied researcher. However, in many scenarios data are incomplete which further complicates the task of model selection. In this talk, we will specify the problem of model selection in incomplete data and discuss several possible solutions using multiple imputation. First, we will define a new general measure for the correct model selec...
متن کاملFormal and Informal Model Selection with Incomplete Data
Model selection and assessment with incomplete data pose challenges in addition to the ones encountered with complete data. There are two main reasons for this. First, many models describe characteristics of the complete data, in spite of the fact that only an incomplete subset is observed. Direct comparison between model and data is then less than straightforward. Second, many commonly used mo...
متن کاملAn Akaike Information Criterion for Model Selection in the Presence of Incomplete Data Title: an Aic for Model Selection with Incomplete Data
We derive and investigate a variant of AIC, the Akaike information criterion, for model selection in settings where the observed data is incomplete. Our variant is based on the motivation provided for the PDIO (\predictive divergence for incomplete observation models") criterion of Shimodaira variant diiers from PDIO in its \goodness-of-t" term. Unlike AIC and PDIO, which require the computatio...
متن کاملA Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data
The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...
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: Journal of the American Statistical Association
سال: 2015
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2014.948545