323 citations · 394 across the 21 of their papers we have counts for
25 papers
Bayesian Nonparametric Marked Hawkes Processes for Earthquake Modeling
Hyotae Kim, Athanasios Kottas
The Hawkes process is a versatile stochastic model for point patterns that exhibit self-excitation, that is, the property that an event occurrence increases the rate of occurrence…
Bayesian Nonparametric Risk Assessment in Developmental Toxicity Studies with Ordinal Responses
Jizhou Kang, Athanasios Kottas
We develop a nonparametric Bayesian modeling framework for clustered ordinal responses in developmental toxicity studies, which typically exhibit extensive heterogeneity. The prima…
Mixture Modeling for Temporal Point Processes with Memory
Xiaotian Zheng, Athanasios Kottas, Bruno Sansó
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 t…
Flexible Bayesian Modeling for Longitudinal Binary and Ordinal Responses
Jizhou Kang, Athanasios Kottas
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…
Bayesian Nonparametric Erlang Mixture Modeling for Survival Analysis
Yunzhe Li, Juhee Lee, Athanasios Kottas
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 s…
Structured Mixture of Continuation-ratio Logits Models for Ordinal Regression
Jizhou Kang, Athanasios Kottas
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 probabi…
Bayesian Geostatistical Modeling for Discrete-Valued Processes
Xiaotian Zheng, Athanasios Kottas, Bruno Sansó
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 (NN…
Erlang mixture modeling for Poisson process intensities
Hyotae Kim, Athanasios Kottas
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 sha…
Nearest-Neighbor Mixture Models for Non-Gaussian Spatial Processes
Xiaotian Zheng, Athanasios Kottas, Bruno Sansó
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…
Modelling for Poisson process intensities over irregular spatial domains
Chunyi Zhao, Athanasios Kottas
We develop nonparametric Bayesian modelling approaches for Poisson processes, using weighted combinations of structured beta densities to represent the point process intensity func…
On Construction and Estimation of Stationary Mixture Transition Distribution Models
Xiaotian Zheng, Athanasios Kottas, Bruno Sansó
Mixture transition distribution time series models build high-order dependence through a weighted combination of first-order transition densities for each one of a specified number…
Autoregressive Density Modeling with the Gaussian Process Mixture Transition Distribution
Matthew Heiner, Athanasios Kottas
We develop a mixture model for transition density approximation, together with soft model selection, in the presence of noisy and heterogeneous nonlinear dynamics. Our model builds…
Bayesian Nonparametric Density Autoregression with Lag Selection
Matthew Heiner, Athanasios Kottas
We develop a Bayesian nonparametric autoregressive model applied to flexibly estimate general transition densities exhibiting nonlinear lag dependence. Our approach is related to B…
Estimation and selection for high-order Markov chains with Bayesian mixture transition distribution models
Matthew Heiner, Athanasios Kottas
We develop two models for Bayesian estimation and selection in high-order, discrete-state Markov chains. Both are based on the mixture transition distribution, which constructs a t…
A New Family of Error Distributions for Bayesian Quantile Regression
Yifei Yan, Athanasios Kottas
We propose a new family of error distributions for model-based quantile regression, which is constructed through a structured mixture of normal distributions. The construction enab…
A Bayesian Nonparametric Markovian Model for Nonstationary Time Series
Maria DeYoreo, Athanasios Kottas
Stationary time series models built from parametric distributions are, in general, limited in scope due to the assumptions imposed on the residual distribution and autoregression r…
Modeling for Dynamic Ordinal Regression Relationships: An Application to Estimating Maturity of Rockfish in California
Maria DeYoreo, Athanasios Kottas
We develop a Bayesian nonparametric framework for modeling ordinal regression relationships which evolve in discrete time. The motivating application involves a key problem in fish…
Modeling for seasonal marked point processes: An analysis of evolving hurricane occurrences
Sai Xiao, Athanasios Kottas, Bruno Sansó
Seasonal point processes refer to stochastic models for random events which are only observed in a given season. We develop nonparametric Bayesian methodology to study the dynamic…
Bayesian nonparametric mean residual life regression
Valerie Poynor, Athanasios Kottas
The mean residual life function is a key functional for a survival distribution. It has a practically useful interpretation as the expected remaining lifetime given survival up to…
Nonparametric Bayesian Inference for Mean Residual Life Functions in Survival Analysis
Valerie Poynor, Athanasios Kottas
The mean residual life function is a key functional for a survival distribution. It has a practically useful interpretation as the expected remaining lifetime given survival up to…
Bayesian Nonparametric Modeling for Multivariate Ordinal Regression
Maria DeYoreo, Athanasios Kottas
Univariate or multivariate ordinal responses are often assumed to arise from a latent continuous parametric distribution, with covariate effects which enter linearly. We introduce…
A Fully Nonparametric Modelling Approach to Binary Regression
Maria DeYoreo, Athanasios Kottas
We propose a general nonparametric Bayesian framework for binary regression, which is built from modeling for the joint response-covariate distribution. The observed binary respons…
The Neutron Star Mass Distribution
Bulent Kiziltan, Athanasios Kottas, Maria De Yoreo +1
In recent years, the number of pulsars with secure mass measurements has increased to a level that allows us to probe the underlying neutron star (NS) mass distribution in detail.…
Mixture Modeling for Marked Poisson Processes
Matthew A. Taddy, Athanasios Kottas
We propose a general modeling framework for marked Poisson processes observed over time or space. The modeling approach exploits the connection of the nonhomogeneous Poisson proces…