DDPstar-package: Density Regression via Dirichlet Process Mixtures of Normal...

In DDPstar: Density Regression via Dirichlet Process Mixtures of Normal Structured Additive Regression Models

Density Regression via Dirichlet Process Mixtures of Normal Structured Additive Regression Models

Description

Implements a flexible, versatile, and computationally tractable model for density regression based on a single-weights dependent Dirichlet process mixture of normal distributions model for univariate continuous responses. The model assumes an additive structure for the mean of each mixture component and the effects of continuous covariates are captured through smooth nonlinear functions. The key components of our modelling approach are penalised B-splines and their bivariate tensor product extension. The proposed method can also easily deal with parametric effects of categorical covariates, linear effects of continuous covariates, interactions between categorical and/or continuous covariates, varying coefficient terms, and random effects. Please see Rodriguez-Alvarez, Inacio et al. (2025) for more details.

Details

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Author(s)

Maria Xose Rodriguez-Alvarez [aut, cre] (<https://orcid.org/0000-0002-1329-9238>), Vanda Inacio [aut] (<https://orcid.org/0000-0001-8084-1616>)

Maintainer: Maria Xose Rodriguez-Alvarez <[e-mail]>

References

Eilers, P.H.C. and Marx, B.D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89-121.

Eilers, P.H.C. and Marx, B.D. (2003). Multidimensional calibration with temperature interaction using two- dimensional penalized signal regression. Chemometrics and Intelligence Laboratory Systems, 66, 159-174.

De Iorio, M., Muller, P., Rosner, G. L., and MacEachern, S. N. (2004). An anova model for dependent random measures. Journal of the American Statistical Association, 99(465), 205-215

De Iorio, M., Johnson, W. O., Muller, P., and Rosner, G. L. (2009). Bayesian nonparametric nonproportional hazards survival modeling. Biometrics, 65, 762–775.

Lang, S. and Brezger, A. (2004). Bayesian P-splines. Journal of Computational and Graphical Statistics, 13(1), 183-212.

Lee, D.-J., Durban, M., and Eilers, P. (2013). Efficient two-dimensional smoothing with P- spline ANOVA mixed models and nested bases. Computational Statistics & Data Analysis, 61, 22-37.

Rodriguez-Alvarez, M. X, Inacio, V. and Klein N. (2025). Density regression via Dirichlet process mixtures of normal structured additive regression models. Accepted for publication in Statistics and Computing (DOI: 10.1007/s11222-025-10567-0).

DDPstar documentation built on April 3, 2025, 8:46 p.m.