• Title/Summary/Keyword: Penalized likelihood

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Tests of Hypotheses in Multiple Samples based on Penalized Disparities

  • Park, Chanseok;Ayanendranath Basu;Ian R. Harris
    • Journal of the Korean Statistical Society
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    • v.30 no.3
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    • pp.347-366
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    • 2001
  • Robust analogues of the likelihood ratio test are considered for testing of hypotheses involving multiple discrete distributions. The test statistics are generalizations of the Hellinger deviance test of Simpson(1989) and disparity tests of Lindsay(1994), obtained by looking at a 'penalized' version of the distances; harris and Basu (1994) suggest that the penalty be based on reweighting the empty cells. The results show that often the tests based on the ordinary and penalized distances enjoy better robustness properties than the likelihood ratio test. Also, the tests based on the penalized distances are improvements over those based on the ordinary distances in that they are much closer to the likelihood ratio tests at the null and their convergence to the x$^2$ distribution appears to be dramatically faster; extensive simulation results show that the improvement in performance of the tests due to the penalty is often substantial in small samples.

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Variable selection in Poisson HGLMs using h-likelihoood

  • Ha, Il Do;Cho, Geon-Ho
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.6
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    • pp.1513-1521
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    • 2015
  • Selecting relevant variables for a statistical model is very important in regression analysis. Recently, variable selection methods using a penalized likelihood have been widely studied in various regression models. The main advantage of these methods is that they select important variables and estimate the regression coefficients of the covariates, simultaneously. In this paper, we propose a simple procedure based on a penalized h-likelihood (HL) for variable selection in Poisson hierarchical generalized linear models (HGLMs) for correlated count data. For this we consider three penalty functions (LASSO, SCAD and HL), and derive the corresponding variable-selection procedures. The proposed method is illustrated using a practical example.

Mean estimation of small areas using penalized spline mixed-model under informative sampling

  • Chytrasari, Angela N.R.;Kartiko, Sri Haryatmi;Danardono, Danardono
    • Communications for Statistical Applications and Methods
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    • v.27 no.3
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    • pp.349-363
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    • 2020
  • Penalized spline is a suitable nonparametric approach in estimating mean model in small area. However, application of the approach in informative sampling in a published article is uncommon. We propose a semiparametric mixed-model using penalized spline under informative sampling to estimate mean of small area. The response variable is explained in terms of mean model, informative sample effect, area random effect and unit error. We approach the mean model by penalized spline and utilize a penalized spline function of the inclusion probability to account for the informative sample effect. We determine the best and unbiased estimators for coefficient model and derive the restricted maximum likelihood estimators for the variance components. A simulation study shows a decrease in the average absolute bias produced by the proposed model. A decrease in the root mean square error also occurred except in some quadratic cases. The use of linear and quadratic penalized spline to approach the function of the inclusion probability provides no significant difference distribution of root mean square error, except for few smaller samples.

H-likelihood approach for variable selection in gamma frailty models

  • Ha, Il-Do;Cho, Geon-Ho
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.1
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    • pp.199-207
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    • 2012
  • Recently, variable selection methods using penalized likelihood with a shrink penalty function have been widely studied in various statistical models including generalized linear models and survival models. In particular, they select important variables and estimate coefficients of covariates simultaneously. In this paper, we develop a penalize h-likelihood method for variable selection in gamma frailty models. For this we use the smoothly clipped absolute deviation (SCAD) penalty function, which satisfies a good property in variable selection. The proposed method is illustrated using simulation study and a practical data set.

Penalized-Likelihood Image Reconstruction for Transmission Tomography Using Spline Regularizers (스플라인 정칙자를 사용한 투과 단층촬영을 위한 벌점우도 영상재구성)

  • Jung, J.E.;Lee, S.-J.
    • Journal of Biomedical Engineering Research
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    • v.36 no.5
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    • pp.211-220
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    • 2015
  • Recently, model-based iterative reconstruction (MBIR) has played an important role in transmission tomography by significantly improving the quality of reconstructed images for low-dose scans. MBIR is based on the penalized-likelihood (PL) approach, where the penalty term (also known as the regularizer) stabilizes the unstable likelihood term, thereby suppressing the noise. In this work we further improve MBIR by using a more expressive regularizer which can restore the underlying image more accurately. Here we used a spline regularizer derived from a linear combination of the two-dimensional splines with first- and second-order spatial derivatives and applied it to a non-quadratic convex penalty function. To derive a PL algorithm with the spline regularizer, we used a separable paraboloidal surrogates algorithm for convex optimization. The experimental results demonstrate that our regularization method improves reconstruction accuracy in terms of both regional percentage error and contrast recovery coefficient by restoring smooth edges as well as sharp edges more accurately.

Choosing the Tuning Constant by Laplace Approximation

  • Ahn, Sung-Mahn;Kwon, Suhn-Beom
    • Communications for Statistical Applications and Methods
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    • v.19 no.4
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    • pp.597-605
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    • 2012
  • Evidence framework enables us to determine the tuning constant in a penalized likelihood formula. We apply the framework to the estimating parameters of normal mixtures. Evidence, which is a solely data-dependent measure, can be evaluated by Laplace approximation. According to a synthetic data simulation, we found that the proper values of the tuning constant can be systematically obtained.

Kernel Poisson regression for mixed input variables

  • Shim, Jooyong
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.6
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    • pp.1231-1239
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    • 2012
  • An estimating procedure is introduced for kernel Poisson regression when the input variables consist of numerical and categorical variables, which is based on the penalized negative log-likelihood and the component-wise product of two different types of kernel functions. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is linearly and/or nonlinearly related to the input variables. Experimental results are then presented which indicate the performance of the proposed kernel Poisson regression.

Cox proportional hazard model with L1 penalty

  • Hwang, Chang-Ha;Shim, Joo-Yong
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.3
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    • pp.613-618
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    • 2011
  • The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

Mixed Effects Kernel Binomial Regression

  • Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.4
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    • pp.1327-1334
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    • 2008
  • Mixed effect binomial regression models are widely used for analysis of correlated count data in which the response is the result of a series of one of two possible disjoint outcomes. In this paper, we consider kernel extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.

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Kernel Machine for Poisson Regression

  • Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.3
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    • pp.767-772
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    • 2007
  • A kernel machine is proposed as an estimating procedure for the linear and nonlinear Poisson regression, which is based on the penalized negative log-likelihood. The proposed kernel machine provides the estimate of the mean function of the response variable, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation(GCV) function of MSE-type is introduced to determine hyperparameters which affect the performance of the machine. Experimental results are then presented which indicate the performance of the proposed machine.

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