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article

Learning latent heterogeneity for type 2 diabetes patients using longitudinal health markers in electronic health records

Jitong Lou, Yuanjia Wang, Lang Li, Donglin Zeng

Statistics in Medicine > 40 > 8 > 1930 - 1946

Electronic health records (EHRs) from type 2 diabetes (T2D) patients consist of longitudinally and sparsely measured health markers at clinical encounters. Our goal is to use such data to learn latent patterns that can inform patient's health status related to T2D while accounting for challenges in retrospectively collected EHRs. To handle challenges such as correlated longitudinal measurements, irregular...

article

Dynamic modeling of multivariate dimensions and their temporal relationships using latent processes: Application to Alzheimer's disease

Bachirou O. Taddé, Hélène Jacqmin‐Gadda, Jean‐François Dartigues, Daniel Commenges, more

Biometrics > 76 > 3 > 886 - 899

Alzheimer's disease gradually affects several components including the cerebral dimension with brain atrophies, the cognitive dimension with a decline in various functions, and the functional dimension with impairment in the daily living activities. Understanding how such dimensions interconnect is crucial for Alzheimer's disease research. However, it requires to simultaneously capture the dynamic...

article

Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States

Xu Gao, Babak Shahbaba, Hernando Ombao

Journal of Classification > 2018 > 35 > 3 > 549-579

Motivated by the problem of predicting sleep states, we develop a mixed effects model for binary time series with a stochastic component represented by a Gaussian process. The fixed component captures the effects of covariates on the binary-valued response. The Gaussian process captures the residual variations in the binary response that are not explained by covariates and past realizations. We develop...

article

Bayesian Single Changepoint Estimation in a Parameter‐driven Model

Chigozie E. Utazi

Scandinavian Journal of Statistics > 44 > 3 > 765 - 779

In this paper, we consider the problem of estimating a single changepoint in a parameter‐driven model. The model – an extension of the Poisson regression model – accounts for serial correlation through a latent process incorporated in its mean function. Emphasis is placed on the changepoint characterization with changes in the parameters of the model. The model is fully implemented within the Bayesian...

article

Multi-scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures

Vianey Leos-Barajas, Eric J. Gangloff, Timo Adam, Roland Langrock, more

Journal of Agricultural, Biological and Environmental Statistics > 2017 > 22 > 3 > 232-248

Hidden Markov models (HMMs) are commonly used to model animal movement data and infer aspects of animal behavior. An HMM assumes that each data point from a time series of observations stems from one of N possible states. The states are loosely connected to behavioral modes that manifest themselves at the temporal resolution at which observations are made. Due to advances in tag technology and tracking...

article

Bayesian joint ordinal and survival modeling for breast cancer risk assessment

C. Armero, C. Forné, M. Rué, A. Forte, more

Statistics in Medicine > 35 > 28 > 5267 - 5282

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional‐odds cumulative logit model. Time‐to‐event is modeled through a left‐truncated proportional‐hazards model, which incorporates information of the longitudinal marker as well...

chapter

Extracting biologically meaningful features from time-series measurements of individual animals: towards quantitative description of animal status

N. C. Friggens, M. C. Codrea, S. Højsgaard

Modelling nutrient digestion and utilisation in farm animals > Advances in methodological aspects of modelling > 40-48

Our ability to describe differences between animal phenotypes is limited, especially in the context of applying nutritional models to predict performance of different genotypes in different environments. This may reflect the diffculty of measuring animal parameters relative to the ease of measuring feed characteristics. However, recent advances in on-farm technology now provide us with multiple time-series...

article

Latent Process Modelling of Threshold Exceedances in Hourly Rainfall Series

Paola Bortot, Carlo Gaetan

Journal of Agricultural, Biological and Environmental Statistics > 2016 > 21 > 3 > 531-547

Two features are often observed in analyses of both daily and hourly rainfall series. One is the tendency for the strength of temporal dependence to decrease when looking at the series above increasing thresholds. The other is the empirical evidence for rainfall extremes to approach independence at high enough levels. To account for these features, Bortot and Gaetan (Scand J Stat 41:606–621, 2014)...

chapter

Credit Contagion in a Long Range Dependent Macroeconomic Factor Model

Francesca Biagini, Serena Fuschini, Claudia Klüppelberg

Advanced Mathematical Methods for Finance > 105-132

We propose a new default contagion model, where default may originate from the performance of a specific firm itself but can also be directly influenced by defaults of other firms. The default intensities of our model depend on smoothly varying macroeconomic variables, driven by a long-range dependent process. In particular, we focus on the pricing of defaultable derivatives whose defaults depend...

article

A data fusion approach for spatial analysis of speciated PM_2.5 across time

Colin W. Rundel, Erin M. Schliep, Alan E. Gelfand, David M. Holland

Environmetrics > 26 > 8 > 515 - 525

PM_2.5 exposure is linked to a number of adverse health effects such as lung cancer and cardiovascular disease. However, PM_2.5 is a complex mixture of different species whose composition varies substantially in both space and time. An open question is how these constituent species contribute to the overall negative health outcomes seen from PM_2.5 exposure. To this end, the Environmental Protection...

article

Structured functional principal component analysis

Haochang Shou, Vadim Zipunnikov, Ciprian M. Crainiceanu, Sonja Greven

Biometrics > 71 > 1 > 247 - 257

Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of...

article

A skew INAR(1) process on $${\mathbb {Z}}$$ Z

Wagner Barreto-Souza, Marcelo Bourguignon

AStA Advances in Statistical Analysis > 2015 > 99 > 2 > 189-208

Integer-valued time series models have been a recurrent theme considered in many papers in the last three decades, but only a few of them have dealt with models on $${\mathbb {Z}}$$ Z (that is, including both negative and positive integers). Our aim in this paper is to introduce a first-order, integer-valued autoregressive process on $${\mathbb {Z}}$$ Z with skew discrete Laplace...

article

Modeling individual tree growth by fusing diameter tape and increment core data

Erin M. Schliep, Tracy Qi Dong, Alan E. Gelfand, Fan Li

Environmetrics > 25 > 8 > 610 - 620

Tree growth estimation is a challenging task as difficulties associated with data collection and inference often result in inaccurate estimates. Two main methods for tree growth estimation are diameter tape measurements and increment cores. The former involves repeatedly measuring tree diameters with a cloth or metal tape whose scale has been adjusted to give diameter readings directly. This approach...

article

On binary and categorical time series models with feedback

Theodoros Moysiadis, Konstantinos Fokianos

Journal of Multivariate Analysis > 2014 > 131 > Complete > 209-228

We study the problem of ergodicity, stationarity and maximum likelihood estimation for multinomial logistic models that include a latent process. Our work includes various models that have been proposed for the analysis of binary and, more general, categorical time series. We give verifiable ergodicity and stationarity conditions for the analysis of such time series data. In addition, we study maximum...

article

A PARAMETER‐DRIVEN LOGIT REGRESSION MODEL FOR BINARY TIME SERIES

Rongning Wu, Yunwei Cui

Journal of Time Series Analysis > 35 > 5 > 462 - 477

We consider a parameter‐driven regression model for binary time series, where serial dependence is introduced by an autocorrelated latent process incorporated into the logit link function. Unlike in the case of parameter‐driven Poisson log‐linear or negative binomial logit regression model studied in the literature for time series of counts, generalized linear model (GLM) estimation of the regression...

article

Chronic use of benzodiazepines and latent cognitive decline in the elderly: Results from the Three-city study

Thibault Mura, Cécile Proust-Lima, Tasnime Akbaraly, Hélène Amieva, more

European Neuropsychopharmacology > 2013 > 23 > 3 > 212-223

We aimed to examine whether long-term use of benzodiazepines is associated with an accelerated decline of cognitive performances by using a statistical model specifically adapted to multivariate longitudinal bounded quantitative outcomes. The data came from the “Three-city” study, a French population based study. All the subjects were 65 years old or older at inclusion and had been followed-up for...

article

On variance estimation in a negative binomial time series regression model

Rongning Wu

Journal of Multivariate Analysis > 2012 > 112 > Complete > 145-155

We study variance estimation in a negative binomial regression model for analyzing time series of counts, where serial dependence among the observed counts is introduced by an autocorrelated latent process. The regression coefficient vector is estimated by maximizing the pseudo-likelihood with the latent process suppressed. The resulting estimator is referred to as the generalized linear model estimator,...

article

A class of stochastic volatility models for environmental applications

Wenying Huang, Ke Wang, F. Jay Breidt, Richard A. Davis

Journal of Time Series Analysis > 32 > 4 > 364 - 377

Many environmental data sets have a continuous domain, in time and/or space, and complex features that may be poorly modelled with a stationary (in space and time) Gaussian process (GP). We adapt stochastic volatility modelling to this context, resulting in a stochastic heteroscedastic process (SHP), which is unconditionally stationary and non‐Gaussian. Conditional on a latent GP, the SHP is a heteroscedastic...

article

Blockwise empirical likelihood for time series of counts

Rongning Wu, Jiguo Cao

Journal of Multivariate Analysis > 2011 > 102 > 3 > 661-673

Time series of counts have a wide variety of applications in real life. Analyzing time series of counts requires accommodations for serial dependence, discreteness, and overdispersion of data. In this paper, we extend blockwise empirical likelihood (Kitamura, 1997 [15]) to the analysis of time series of counts under a regression setting. In particular, our contribution is the extension of Kitamura’s...

article

Semi‐parametric latent process model for longitudinal ordinal data: Application to cognitive decline

Hélène Jacqmin‐Gadda, Cécile Proust‐Lima, Hélène Amiéva

Statistics in Medicine > 29 > 26 > 2723 - 2731

Ordinal and quantitative discrete data are frequent in biomedical and neuropsychological studies. We propose a semi‐parametric model for the analysis of the change over time of such data in longitudinal studies. A threshold model is defined where the outcome value depends on the current value of an underlying Gaussian latent process. The latent process model is a Gaussian linear mixed model with a...

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Search results

Learning latent heterogeneity for type 2 diabetes patients using longitudinal health markers in electronic health records

Dynamic modeling of multivariate dimensions and their temporal relationships using latent processes: Application to Alzheimer's disease

Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States

Bayesian Single Changepoint Estimation in a Parameter‐driven Model

Multi-scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures

Bayesian joint ordinal and survival modeling for breast cancer risk assessment

Extracting biologically meaningful features from time-series measurements of individual animals: towards quantitative description of animal status

Latent Process Modelling of Threshold Exceedances in Hourly Rainfall Series

Credit Contagion in a Long Range Dependent Macroeconomic Factor Model

A data fusion approach for spatial analysis of speciated PM_2.5 across time

Structured functional principal component analysis

A skew INAR(1) process on $${\mathbb {Z}}$$ Z

Modeling individual tree growth by fusing diameter tape and increment core data

On binary and categorical time series models with feedback

A PARAMETER‐DRIVEN LOGIT REGRESSION MODEL FOR BINARY TIME SERIES

Chronic use of benzodiazepines and latent cognitive decline in the elderly: Results from the Three-city study

On variance estimation in a negative binomial time series regression model

A class of stochastic volatility models for environmental applications

Blockwise empirical likelihood for time series of counts

Semi‐parametric latent process model for longitudinal ordinal data: Application to cognitive decline

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