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We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM) with a subset of variables truncated to be nonnegative. The…

Machine Learning · Statistics 2016-11-22 Qinliang Su , Xuejun Liao , Changyou Chen , Lawrence Carin

In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions. Conventional inferential methods for DGP models can suffer from high computational complexity as they require…

Machine Learning · Statistics 2023-05-02 Liang Ding , Rui Tuo , Shahin Shahrampour

The purpose of the present work is to construct estimators for the random effects in a fractional diffusion model using a hybrid estimation method where we combine parametric and nonparametric thechniques. We precisely consider $n$…

Statistics Theory · Mathematics 2025-06-13 Nesrine Chebli , Hamdi Fathallah , Yousri Slaoui

We introduce a new R package useful for inference about network count time series. Such data are frequently encountered in statistics and they are usually treated as multivariate time series. Their statistical analysis is based on linear or…

Methodology · Statistics 2023-10-26 Mirko Armillotta , Michail Tsagris , Konstantinos Fokianos

For random piecewise linear systems T of the interval that are expanding on average we construct explicitly the density functions of absolutely continuous T-invariant measures. In case the random system uses only expanding maps our…

Dynamical Systems · Mathematics 2023-06-22 Charlene Kalle , Marta Maggioni

The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian process. We propose a kernel based maximum entropy spectral estimator. The latter searches the optimal spectrum over a class of high order…

Optimization and Control · Mathematics 2020-04-30 Mattia Zorzi

Although models for count data with over-dispersion have been widely considered in the literature, models for under-dispersion -- the opposite phenomenon -- have received less attention as it is only relatively common in particular research…

Transformed Generalized Autoregressive Moving Average (TGARMA) models were recently proposed to deal with non-additivity, non-normality and heteroscedasticity in real time series data. In this paper, a Bayesian approach is proposed for…

Applications · Statistics 2017-01-02 Breno S. Andrade , Marinho G. Andrade , Ricardo S. Ehlers

This paper introduces an R package SensIAT that implements a sensitivity analysis methodology, based on augmented inverse intensity weighting, for randomized trials with irregular and potentially informative assessment times. Targets of…

This article describes the R package htmcglm implemented for performing hypothesis tests on regression and dispersion parameters of multivariate covariance generalized linear models (McGLMs). McGLMs provide a general statistical modeling…

Computation · Statistics 2022-08-03 Lineu Alberto Cavazani de Freitas , Wagner Hugo Bonat

In this paper, we explore the application of Gaussian Processes (GPs) for predicting mean-reverting time series with an underlying structure, using relatively unexplored functional and augmented data structures. While many conventional…

Statistical Finance · Quantitative Finance 2024-03-05 Narayan Tondapu

The exponential random graph (ERGM) model is a commonly used statistical framework for studying the determinants of tie formations from social network data. To test scientific theories under the ERGM framework, statistical inferential…

Methodology · Statistics 2023-12-01 Joris Mulder , Nial Friel , Philip Leifeld

Near-Gaussian probability densities are common in many important physical applications. Here we develop an asymptotic expansion methodology for computing entropic functionals for such densities. The expansion proposed is a close relative of…

Statistics Theory · Mathematics 2016-06-29 Gordon V. Chavez , Richard Kleeman

The package High-dimensional Metrics (\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence…

Machine Learning · Statistics 2017-09-28 Victor Chernozhukov , Chris Hansen , Martin Spindler

Motivated by investigating spatio-temporal patterns of the distribution of continuous variables, we consider describing the conditional distribution function of the response variable incorporating spatio-temporal components given…

We present an R package bssm for Bayesian non-linear/non-Gaussian state space modelling. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Gaussian approximations such as the Laplace approximation and…

Computation · Statistics 2022-04-13 Jouni Helske , Matti Vihola

BACKGROUND: Random-effects meta-analysis within a hierarchical normal modeling framework is commonly implemented in a wide range of evidence synthesis applications. More general problems may even be tackled when considering meta-regression…

Computation · Statistics 2022-12-27 Christian Röver , Tim Friede

Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision matrices. We develop a Bayesian method to incorporate covariate information in this GGMs setup in a nonlinear…

The Unit-Lindley is a one-parameter family of distributions in $(0,1)$ obtained from an appropriate transformation of the Lindley distribution. In this work, we introduce a class of dynamical time series models for continuous random…

Statistics Theory · Mathematics 2025-04-11 Guilherme Pumi , Danilo Hiroshi Matsuoka , Taiane Schaedler Prass

This work proposes a scalable probabilistic latent variable model based on Gaussian processes (Lawrence, 2004) in the context of multiple observation spaces. We focus on an application in astrophysics where data sets typically contain both…

Astrophysics of Galaxies · Physics 2025-02-28 Vidhi Lalchand , Anna-Christina Eilers