Zobrazeno 1 - 10
of 239
pro vyhledávání: '"Kottas, Athanasios"'
Autor:
Kang, Jizhou, Kottas, Athanasios
We develop a nonparametric Bayesian modeling framework for clustered ordinal responses in developmental toxicity studies, which typically exhibit extensive heterogeneity. The primary focus of these studies is to examine the dose-response relationship
Externí odkaz:
http://arxiv.org/abs/2408.11803
We propose a constructive approach to building temporal point processes that incorporate dependence on their history. The dependence is modeled through the conditional density of the duration, i.e., the interval between successive event times, using
Externí odkaz:
http://arxiv.org/abs/2407.03774
Autor:
Kang, Jizhou, Kottas, Athanasios
Longitudinal studies with binary or ordinal responses are widely encountered in various disciplines, where the primary focus is on the temporal evolution of the probability of each response category. Traditional approaches build from the generalized
Externí odkaz:
http://arxiv.org/abs/2307.00224
We develop a flexible Erlang mixture model for survival analysis. The model for the survival density is built from a structured mixture of Erlang densities, mixing on the integer shape parameter with a common scale parameter. The mixture weights are
Externí odkaz:
http://arxiv.org/abs/2211.08652
Autor:
Kang, Jizhou, Kottas, Athanasios
We develop a nonparametric Bayesian modeling approach to ordinal regression based on priors placed directly on the discrete distribution of the ordinal responses. The prior probability models are built from a structured mixture of multinomial distrib
Externí odkaz:
http://arxiv.org/abs/2211.04034
We introduce a flexible and scalable class of Bayesian geostatistical models for discrete data, based on the class of nearest neighbor mixture transition distribution processes (NNMP), referred to as discrete NNMP. The proposed class characterizes sp
Externí odkaz:
http://arxiv.org/abs/2111.01840
Autor:
Kim, Hyotae, Kottas, Athanasios
We develop a prior probability model for temporal Poisson process intensities through structured mixtures of Erlang densities with common scale parameter, mixing on the integer shape parameters. The mixture weights are constructed through increments
Externí odkaz:
http://arxiv.org/abs/2110.12513
We develop a class of nearest-neighbor mixture models that provide direct, computationally efficient, probabilistic modeling for non-Gaussian geospatial data. The class is defined over a directed acyclic graph, which implies conditional independence
Externí odkaz:
http://arxiv.org/abs/2107.07736
Autor:
Zhao, Chunyi, Kottas, Athanasios
We develop nonparametric Bayesian modelling approaches for Poisson processes, using weighted combinations of structured beta densities to represent the point process intensity function. For a regular spatial domain, such as the unit square, the model
Externí odkaz:
http://arxiv.org/abs/2106.04654
Publikováno v:
In Computational Statistics and Data Analysis March 2024 191
