Add to ORKGThe paper deals with parameter estimation for categor-ical data under epistemic data imprecision, where for a part of the data only coarse(ned) versions of the true values are observable. For different observation models formalizing the information available on the coarsening process, we derive the (typically set-valued) maximum likelihood estimators of the underlying distributions. We discuss the homogeneous case of independent and identically distributed variables as well as logistic re-gression under a categorical covariate. We start with the imprecise point estimator under an observation model describing the coarsening process without any further assumptions. Then we determine several sen-sitivity parameters that allow the refinement of...
Consider a set of categorical variables P where at least one, denoted by Y, is binary. The log-linea...
This thesis consists of four papers that deal with several aspects of the measurement of model fit f...
We present an EM based solution to missing categorical covariates in Binomial models with logit link...
Paper presented at 9th International Symposium on Imprecise Probability: Theories and Applications, ...
In most surveys, one is confronted with missing or, more generally, coarse data. Many methods dealin...
Logistic regression is an important statistical tool for assessing the probability of an outcome bas...
In some applications, only a coarsened version of a categorical outcome variable can be observed. Pa...
Very often the data collected by social scientists involve dependent observations, without, however,...
The feasibility of maximum likelihood (ML) analyses of marginal distributions of repeated categorica...
Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at r...
Multivariate categorical data occur in many applications of machine learning. One of the main diffic...
Multivariate categorical data occur in many applications of machine learning. One of the main diffic...
Very often the data collected by social scientists involve dependent observations, without, however,...
Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at r...
Consider a set of categorical variables P where at least one, denoted by Y, is binary. The log-linea...
This thesis consists of four papers that deal with several aspects of the measurement of model fit f...
We present an EM based solution to missing categorical covariates in Binomial models with logit link...
Paper presented at 9th International Symposium on Imprecise Probability: Theories and Applications, ...
In most surveys, one is confronted with missing or, more generally, coarse data. Many methods dealin...
Logistic regression is an important statistical tool for assessing the probability of an outcome bas...
In some applications, only a coarsened version of a categorical outcome variable can be observed. Pa...
Very often the data collected by social scientists involve dependent observations, without, however,...
The feasibility of maximum likelihood (ML) analyses of marginal distributions of repeated categorica...
Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at r...
Multivariate categorical data occur in many applications of machine learning. One of the main diffic...
Multivariate categorical data occur in many applications of machine learning. One of the main diffic...
Very often the data collected by social scientists involve dependent observations, without, however,...
Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at r...
Consider a set of categorical variables P where at least one, denoted by Y, is binary. The log-linea...
This thesis consists of four papers that deal with several aspects of the measurement of model fit f...
We present an EM based solution to missing categorical covariates in Binomial models with logit link...