Predicting longitudinal trajectories of health probabilities with random-effects multinomial logit regression
نویسندگان
چکیده
منابع مشابه
Multinomial logit random effects models
This article presents a general approach for logit random effects modelling of clustered ordinal and nominal responses. We review multinomial logit random effects models in a unified form as multivariate generalized linear mixed models. Maximum likelihood estimation utilizes adaptive Gauss–Hermite quadrature within a quasi-Newton maximization algorithm. For cases in which this is computationall...
متن کاملSemantic Scene Segmentation using Random Multinomial Logit
We introduce Random Multinomial Logit (RML), a general multi-class classifier based on an ensemble of multinomial logistic regression models, and apply it to the task of semantic image segmentation. The algorithm is simple, can be trained efficiently, and has near realtime runtime performance. RML combines the desirable properties of multinomial logistic regression, being stable and theoretical...
متن کاملPrice Competition under Multinomial Logit Demand Functions with Random Coefficients
In this paper, we postulate a general class of price competition models with Mixed Multinomial Logit demand functions under affine cost functions. We first characterize the equilibrium behavior of this class of models in the case where each product in the market is sold by a separate, independent firm and customers share a common income level. We identify a simple and very broadly satisfied con...
متن کاملRandom Forests for multiclass classification: Random MultiNomial Logit
Several supervised learning algorithms are suited to classify instances into a multiclass value space. MultiNomial Logit (MNL) is recognized as a robust classifier and is commonly applied within the CRM (Customer Relationship Management) domain. Unfortunately, to date, it is unable to handle huge feature spaces typical of CRM applications. Hence, the analyst is forced to immerse himself into fe...
متن کاملLongitudinal Discriminant Analysis with Random Effects for Predicting Preeclampsia using Hematocrit Data
Background and Objectives: Preeclampsia is the third leading cause of death in pregnant women. This study was conducted to evaluate the ability of longitudinal hematocrit data to predict preeclampsia and to compare the accuracy in longitudinal and cross-sectional data. Materials and Methods: In a prospective cohort study from October 2010 to July 2011, 650 pregnant women referred to the prenata...
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: Statistics in Medicine
سال: 2012
ISSN: 0277-6715
DOI: 10.1002/sim.5514