Author
Karin Ayumi Tamura
Bio: Karin Ayumi Tamura is an academic researcher from University of São Paulo. The author has contributed to research in topics: Logistic regression & Random effects model. The author has an hindex of 2, co-authored 2 publications receiving 15 citations.
Papers
More filters
TL;DR: A new methodology is proposed based on linear regression that considers the relationship among the random effects and the covariates aggregated at the group level, and indicates that LRPM drastically reduced the computational effort, and at the same time, maintained a similar level of prediction in relation to EBP.
12 citations
TL;DR: A new method to predict the response variable of an observation in a new cluster for a multilevel logistic regression based on the empirical best estimator for the random effect is presented.
Abstract: The purpose of this article is to present a new method to predict the response variable of an observation in a new cluster for a multilevel logistic regression. The central idea is based on the empirical best estimator for the random effect. Two estimation methods for multilevel model are compared: penalized quasi-likelihood and Gauss–Hermite quadrature. The performance measures for the prediction of the probability for a new cluster observation of the multilevel logistic model in comparison with the usual logistic model are examined through simulations and an application.
3 citations
Cited by
More filters
TL;DR: The problem of estimating small area non linear parameters is treated, with special emphasis on the estimation of poverty proportions, and borrowing strength from time by using area-level linear time models is proposed.
69 citations
TL;DR: This method can be well-behaved to automatically identify thresholds in crash prediction, by minimizing the cross entropy between the original dataset with continuous probability of a crash occurring and the binarized dataset after using the thresholds to separate potential crash warnings against normal traffic conditions.
28 citations
Journal Article•
TL;DR: In this article, the use of improved approximations for the estimation of generalized linear multilevel models where the response is a proportion is discussed, and an improved approximation is introduced which largely eliminates the biases in the situation described by Rodriguez and Goldman.
Abstract: SUMMARY This paper discusses the use of improved approximations for the estimation of generalized linear multilevel models where the response is a proportion. Simulation studies by Rodriguez and Goldman have shown that in extreme situations large biases can occur, most notably when the response is binary, the number of level 1 units per level 2 unit is small and the underlying random parameter values are large. An improved approximation is introduced which largely eliminates the biases in the situation described by Rodriguez and Goldman. Keywortis: �BINARY RESPONSE; GENERALIZED LINEAR MODEL; HIERARCHICAL DATA; MARGINAL MODEL; MULTILEVEL MODEL; QUASI-LIKELIHOOD; UNIT-SPECIFIC MODEL
20 citations
TL;DR: This class of semi-mixed effects models constitutes a continuum of models, indexed by a ''slider'', that determines the position of the model between these two extremes, so that the model selected can be close to the parsimonious random effects case, but far enough away from it to filter out unwanted dependences.
18 citations
TL;DR: A new methodology is proposed based on linear regression that considers the relationship among the random effects and the covariates aggregated at the group level, and indicates that LRPM drastically reduced the computational effort, and at the same time, maintained a similar level of prediction in relation to EBP.
12 citations