Results 91 to 100 of about 2,584,664 (216)

Poisson–Birnbaum–Saunders regression model for clustered count data

In this paper we study the number of inpatient admissions by individuals to hospital emergency rooms reported by the 2003 Medical Expenditure Panel Survey (MEPS), which the United States Agency for Health Research and Quality conducts.
Ombao, Hernando, Barreto-Souza, Wagner, Gonçalves, Jussiane Nader   +2 more
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Model Evaluation and Selection in Multiple Nonlinear Regression Analysis

The main problem in regression model selection independently from application domain is finding the best model that best fits the data and does not neither overfit nor underfit.
Jēkabsons, Gints, Lavendels, Jurijs, Šitikovs, Vjačeslavs   +2 more
core  

Alternating model trees

, 2015
Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes.
Kramer, Stefan, Eibe Frank, Michael Mayo, Mayo, Michael, Frank, Eibe, Stefan Kramer   +5 more
core   +1 more source

The Role of Physicochemical Characteristics of Gully Soil and Environmental Factors of its Upper-Catchment Area in the Expansion of Gully Erosion

علوم آب و خاک, 2018
In this research, a regression model was introduced to study the mechanisms of the formation of gullies in the Quri Chay watershed, northern Ardebil province (Moghan Plain); this was done through investigating the effective factors of geo-environment and
B. Farid Giglou, R. Ghazavi
doaj  

The development of an Interval Grey Regression Model for Limited Time Series Forecasting

, 2011
The grey model GM(1, 1) is a popular forecasting method in management and engineering applications. In order to obtain more validity and reliable forecasting values, fuzzy grey regression model is proposed by hybridizing fuzzy set into grey model GM(1, 1)
Tsaur, Ruey-Chyn
core   +1 more source

Sparse least trimmed squares regression. [PDF]

Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common problem in applied statistics is the presence of outliers in the data.
Croux, Christophe, Alfons, Andreas, Gelper, Sarah   +2 more
core  

Least squares estimation of regression coefficients of singular random fields observed on a sphere

, 2001
We present some results on the rate of convergence to the normal law of the least square estimates of the regression coefficient of random fields with long range dependence observed on a ...
Anh, Vo, Sharapov, M, Anh, V.V., Leonenko, N.N., Sharapov, M.M., Leonenko, Nikolai   +5 more
core  

CUB model trees for ordinal response: preliminary results

, 2016
estimate a CUB models within the framework of tree based methods in order to provide a tool for growing trees for ordinal responses in which every node is associated with a CUB regression ...
SIMONE, ROSARIA, CAPPELLI, CARMELA, DI IORIO, FRANCESCA   +2 more
core  

Reducing the bias of the maximum likelihood estimator for the Poisson regression model [PDF]

We derive expressions for the first-order bias of the MLE for a Poisson regression model and show how these can be used to adjust the estimator and reduce bias without increasing MSE.
Hui Feng, David E Giles
core  

A Heteroscedastic Bayesian Generalized Logistic Regression Model with Application to Scaling Problems

Power law scaling models have been used to understand the complexity of systems as diverse as cities, neurological activity, and rainfall and lightning.
Shahtahmassebi, G, Sutton, J., Sutton, Jack, Shahtahmassebi, G., Sutton, J, Ribeiro, H. V., Hanley, Quentin S., Ribeiro, Haroldo V., Shahtahmassebi, Golnaz, Hanley, QS, Hanley, Q. S., Ribeiro, HV   +11 more
core   +1 more source