- Referenced in 191 articles
- CAViaR: Conditional autoregressive value at risk by regression quantiles. Value at risk ... autoregressive process and estimates the parameters with regression quantiles. Utilizing the criterion that each period...
- Referenced in 107 articles
- GAMs and other generalized ridge regression with multiple smoothing parameter selection by GCV, REML...
- Referenced in 20 articles
- conditional maximum likelihood estimation of regression parameters in the conventional EM algorithm for Gaussian data ... least squares (with fixed truncation parameter) and ridge regression with generalized cross-validation as regularized ... that perform he regularized estimation of regression parameters (e.g., ridge regression and generalized cross-validation ... methods and other methods of determiningca regularization parameter. Per-Christian Hansen’s Regularization Tools contain...
- Referenced in 34 articles
- specified by the user (the ”weights”). Regression model parameters from Cox models (PROC PHREG...
- Referenced in 24 articles
- statistics (including t-statistics based on regression parameters from linear and survival models as well...
- Referenced in 317 articles
- unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses ... discussions are devoted to model construction, smoothing parameter selection, computation, and asymptotic convergence. Most ... popular S/S- PLUS language. Code for regression has been distributed in the R package...
- Referenced in 28 articles
- linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality...
- Referenced in 11 articles
- model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral...
- Referenced in 10 articles
- Logistic Regression. Repeated measures proportional odds logistic regression analysis of ordinal score data ... models. The algorithm developed estimates the correlation parameter ... minimizing the generalized variance of the regression parameters at each step of the fitting algorithm...
- Referenced in 38 articles
- number of classification and regression techniques, including machine-readable parameter descriptions. There is also...
- Referenced in 8 articles
- both baseline hazard function and regression parameters...
- Referenced in 4 articles
- Multi-parameter regression survival modeling: an alternative to proportional hazards. It is standard practice ... refer to this practice as “multi-parameter regression” (MPR) modeling and explore ... analysis context. We find that multi-parameter regression leads to more flexible models which ... strategy is introduced for such multi-parameter regression models. It accounts for the correlation arising...
- Referenced in 92 articles
- common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there ... includes methods for tuning based on classical regression and analysis of variance techniques; tree-based...
- Referenced in 9 articles
- likelihood and quasi-likelihood estimation parameters in nonlinear regression models We present Fortran 77 subroutines ... simple bounds on the parameters, and that compute approximate regression diagnostics...
- Referenced in 6 articles
- hierarchical prior distribution on the regression parameters is specifically designed to deal with high dimensionality...
- Referenced in 21 articles
- weighted orthogonal distance regression, i.e., for finding the parameters that minimize...
- Referenced in 72 articles
- data using generalized linear mixed models, and regression data using generalized additive models. The package ... model comparison and for eliciting the precision parameter of the Dirichlet process prior...
- Referenced in 19 articles
- Bivariate Poisson and diagonal inflated bivariate Poisson regression models in R. In this paper ... estimation of the parameters of bivariate and diagonal inflated bivariate Poisson regression models. An Expectation...
- Referenced in 15 articles
- auto-regressive conditional heteroscedastic model for financial returns with piecewise constant parameter values. Our method ... termed BASTA (binary segmentation for transformed auto-regressive conditional heteroscedasticity), proceeds in two stages: process ... simulation to fine-tune their parameters as well as the threshold parameter for the binary...
- Referenced in 4 articles
- single framework allowing for time synchronization of parameters, on-line incremental processing as well ... feature summaries), such as moments, peaks, regression parameters, etc. Postprocessing of the features includes statistical...