[PDF] Top 20 Mean and median bias reduction in generalized linear models
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Mean and median bias reduction in generalized linear models
... the linear programming algorithms in Konis (2007), as implemented in the detect_separation method of the brglm2 R package (Kosmidis ...the bias and coverage of Wald- type confidence intervals for the ML ... See full document
17
Median bias reduction in random effects meta analysis and meta regression
... the median bias-reducing adjusted score functions for random-effects meta-analysis and ...a median BRPL, whose logarithm differs from the logarithm of the mean BRPL in Kosmidis et ...for ... See full document
16
Fitting Models of Vulnerability to Toxicity with Generalized Linear Models
... population mean as toxicity limits), the auxiliary variable were used to “fine- tune” (in the sense that, if four or more of the auxiliary variables are above their toxicity limits, their corresponding y i , ... See full document
12
Statistical Inference in Generalized Linear Mixed Models by Joint Modelling Mean and Covariance of Non Normal Random Effects
... Generalized linear mixed models (GLMMs) are very helpful and widely used for analyzing discrete data and data from exponential family ...hierarchical generalized linear models ... See full document
17
Ridge regression and diagnostics in generalized linear models
... in generalized linear models ...smaller mean squared error than the MLE under certain conditions, an empirical study is conducted to evaluate the ridge estimator in ...prediction mean ... See full document
133
Dirichlet Process Mixtures of Generalized Linear Models
... Again, the DP-GLM was run on the largest data set size tested several times; log posterior probabil- ities were evaluated graphically, and in each case the posterior probabilities seem to have stabilized well before ... See full document
31
Generalized Linear Models in Vehicle Insurance
... years generalized linear models became a favorite statistical tool to model actuarial ...a mean to understand the relationship between risk factors and to calibrate the lapse risk as ... See full document
6
Fit Generalized Linear Models by Using of Different Likelihoods
... Generalized linear models can deal with probit or logistic regression, Poisson regression, logarithmic linear models for cross tables, estimates of the variance component of the ... See full document
5
Multinomial logit bias reduction via Poisson log linear model
... multivariate generalized linear models, after some simple matrix manipulation, shows that these adjusted score functions are identical to those obtained by direct penalization of the likelihood for ... See full document
9
On iterative adjustment of responses for the reduction of bias in binary regression models
... binomial-response generalized linear models with ar- bitrary link ...the bias-reducing adjusted score functions in Kosmidis & Firth (2008, Biometrika) and thus its use guarantees ... See full document
9
Jackknife bias reduction in autoregressive models with a unit root
... the bias of ordinary least squares (OLS) estimators of the parameters in stationary autoregressive (AR) processes has been extensively studied and its properties are well ...the bias in terms of the inverse ... See full document
30
On a Class of Probability Distributions With Application Using Rainfall Data of Kashmir Valley Bilal Ahmad Bhat 1, N. A. Rather2 , T. A. Rather 3
... (1.2) As g(t) and f(t) may be replaced by different sets of p.g.f.s, (1.2) may provide us with families of generalized discrete type of distributions. Some of the important members of this family for some ... See full document
7
A sequential reduction method for inference in generalized linear mixed models
... In the case of a linear mixed model, the approximating normal density is precise, and there is no error in the Laplace approximation to the likelihood. In other cases, and particularly when the response is ... See full document
19
Modelling the mass of kiwi fruit by geometrical attributes
... maximum, mean, and minimum diameters, mass, volume, bulk density, geometric mean and percent sphericity of mixed two different varie- ties of kiwi fruits are shown in Table ... See full document
5
Analysis of Longitudinal Data and Model Selection
... of generalized linear models are not ...in generalized linear models, of which the normal linear regression model is a special ...regression models are also given ... See full document
141
Tests for High Dimensional Generalized Linear Models
... We consider testing regression coefficients in high dimensional generalized linear mod- els. By modifying a test statistic proposed by Goeman et al. (2011) for large but fixed dimensional settings, we ... See full document
39
Generalized linear mixed models for count data
... Theoretically, the EM algorithm is the most accurate method that we have dis- cussed so far. Unlike the Laplace approximation and Gauss-Hermite quadrature, where we have to worry about the coefficients of the error terms ... See full document
97
Quasi Monte Carlo Estimation in Generalized Linear Mixed Model with Correlated Random Effects
... in generalized linear mixed models (GLMMs) is highly challenging because it may involve analytically intractable high-dimen- sional ...joint mean-covariance modelling is ... See full document
16
Differential attentional bias in generalized anxiety disorder and panic disorder
... of other drugs, such as benzodiazepines, might interfere with attentional performance. Therefore, the patients were grouped according to whether they had taken medication or not. Further analysis showed no remarkable ... See full document
8
Non-crystallographic reduction of generalized Calogero-Moser models
... introduced reduction procedure based on the embedding of non-crystallographic Coxeter groups into crystallographic ones to Calogero-Moser sys- ...familiar generalized Calogero Hamiltonian is ... See full document
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