Esther Salazar

U.S. Food and Drug Administration | FDA · Center for Tobacco Products

Introduction: Few studies have examined trends in over-the-counter U.S. Food and Drug Administration‒approved nicotine replacement therapy sales data and consumer preferences for nicotine replacement therapy attributes (e.g., flavor). Examination of consumer preferences may inform both public health smoking cessation programs as well as subsequent...

Introduction Moist snuff smokeless tobacco (ST) products are available in the U.S. in both “loose” and “portioned” (i.e., pouched) formats, but no published study to date has clinically evaluated the associations between ST format, use behavior, and nicotine exposure. Methods Participants used their usual brand of ST (loose ST [n = 30] or portione...

Background: The role of smoking in racial disparities in mortality and life expectancy in the United States has been examined previously, but up-to-date estimates are generally unavailable, even though smoking prevalence has declined in recent decades. Objective: We estimate the contribution of smoking-attributable mortality to observed differen...

An amendment to this paper has been published and can be accessed via the original article.

Background Cigarettes and smokeless tobacco (SLT) products are among a wide range of tobacco products that are addictive and pose a significant health risk. In this study, we estimated smoking- and SLT use-related mortality hazard ratios (HRs) among U.S. adults by sex, age group, and cause of death, for nine mutually exclusive categories of smoking...

The main purpose of this analysis is to quantify quality adjusted life years (QALYs) lost associated with lifetime exclusive cigarette or smokeless tobacco use among U.S. adults. Multiple waves of National Health Interview Survey (NHIS) data linked to death certificate records were used to define current exclusive cigarette and smokeless tobacco us...

Objectives: Due to rapidly emerging electronic nicotine delivery system (ENDS) technologies, increasing use in the US, and the unclear impact on users' health, investigating behavior associated with ad libitum ENDS use is an important research topic. ENDS use behavior is typically assessed either by direct observation or through smoking topography...

Objectives: Little cigars resemble cigarettes but are not subject to the US Food and Drug Administration's flavor restrictions on cigarettes. This within-subject laboratory study assessed the abuse liability of cigarettes and little cigars of varying flavors. Methods: Forty-eight adult cigarette smokers who also smoke little cigars or cigarillos co...

We consider the problem of projecting future climate from ensembles of regional climate model (RCM) simulations using results from the North American Regional Climate Change Assessment Program (NARCCAP). To this end, we develop a hierarchical Bayesian space-time model that quantifies the discrepancies between different members of an ensemble of RCM...

We develop statistical methods for multi-modality assessment of mental health, based on four forms of data: (i) self-reported answers to a set of classical questionnaires , (ii) single-nucleotide polymorphism (SNP) data, (iii) fMRI data measured in response to visual stimuli, and (iv) scores for psychiatric disorders. The data were acquired from hu...

We present a probabilistic framework for learning with heterogeneous multiview data where some views are given as ordinal, binary, or real-valued feature matrices, and some views as similarity matrices. Our framework has the following distinguishing aspects: (i) a unified latent factor model for integrating information from diverse feature (ordinal...

We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (i) discovering topic prevalence over time, and (ii) learning contemporary multi-scale dependence structures, providing topic and word correlations as a byproduct. The high-dimensional and time-evolving ordinal/rank observations (such as word counts), after an...

We develop Bayesian reference analyses for linear regression models when the errors follow an exponential power distribution. Specifically, we obtain explicit expressions for reference priors for all the six possible orderings of the model parameters and show that, associated with these six parameters orderings, there are only two reference priors....

We consider modeling spatio-temporally indexed relational data, motivated by analysis of voting data for the United States House of Representatives over two decades. The data are characterized by incomplete binary matrices, representing votes of legislators on legislation over time. The spatial covariates correspond to the location of a legislator’...

A model is presented for analysis of multivariate binary data with spatio-temporal dependencies, and applied to congressional roll call data from the United States House of Representatives and Senate. The model considers each legislator’s constituency (location), the congressional session (time) of each vote, and the details (text) of each piece of...

This chapter will address the issue of combining information from a possibly large time series with a factor analytic approach. Results obtained from this exercise are a (hopefully much) smaller number of latent time series that represent the main features of the complete dataset of time series originally available. Each combination of a time serie...

A new model is developed for joint analysis of ordered, categorical, real and count data. The ordered and categorical data are answers to questionnaires, the (word) count data correspond to the text questions from the questionnaires, and the real data correspond to fMRI responses for each subject. The Bayesian model employs the von Mises distributi...

We develop objective Bayesian analysis for the linear regression model with random errors distributed according to the exponential power distribution. More specifically, we derive explicit expressions for three different Jeffreys priors for the model parameters. We show that only one of these Jeffreys priors leads to a proper posterior distribution...

We propose a model-based vulnerability index of the population from Uruguay to vector-borne diseases. We have available measurements of a set of variables in the census tract level of the 19 Departmental capitals of Uruguay. In particular, we propose an index that combines different sources of information via a set of micro-environmental indicators...

We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix maps each row-column pair of bina...

We consider the problem of forecasting future regional climate. Our method is based on blending different members of an ensemble of regional climate model (RCM) simulations while accounting for the discrepancies between these simulations, under present day conditions, and observational records for the recent past. To this end, we develop Bayesian s...

We consider the problem of combining multiple climate models to forecast future regional climate. Our method is based on blending different members of an ensemble of regional climate model (RCM) simulations while accounting for the discrepancies between these simulations and observational records under current climate conditions.. To this end, we d...

We consider temporal aggregation of lognormal autoregressive (AR) processes. More specifically, we develop a novel moment‐matching approximation for temporally aggregated lognormal AR processes. In addition, we show that our approximation provides the closest lognormal AR process in terms of Kullback–Leibler divergence. Moreover, we perform a simul...

This paper introduces a new class of spatio-temporal models for measurements belonging to the exponential family of distributions. In this new class, the spatial and temporal components are conditionally independently modeled via a latent factor analysis structure for the (canonical) transformation of the measurements mean function. The factor load...

A new class of space-time models derived from standard dynamic fac-tor models is proposed. The temporal dependence is modeled by latent factors while the spatial dependence is modeled by the factor loadings. Factor analytic arguments are used to help identify temporal components that summarize most of the spatial variation of a given region. The te...

This study applies the proportional odds and partial proportional odds models for ordinal logistic regression to analyze household electricity consumption classes. Micro-data from households situated in the state of Rio de Janeiro during 2004 was used to measure the performance of the models in correctly classifying household electricity consumptio...

In this work we present a flexible class of linear models to treat observations made in discrete time and continuous space, where the regression coefficients vary smoothly in time and space. This kind of model is particularly appealing in situations where the effect of one or more explanatory processes on the response present substantial heterogene...

The Valencia International Meetings on Bayesian Statistics, held every four years, provide the main forum for researchers in the area of Bayesian Statistics to come together to present and discuss frontier developments in the field. Covering a broad range of applications and models, including genetics, computer vision and computation, the resulting...

Hyperparameter estimation in dynamic linear models leads to inference that is not available analytically. Recently, the most common approach is through MCMC approximations. A number of sampling schemes that have been proposed in the literature are compared. They basically differ in their blocking structure. In this paper, comparison between the mos...

In this paper, we propose a Bayesian approach to model the level and the variance of (financial) time series by the special class of nonlinear time series models known as the logistic smooth transition autoregressive models, or simply the LSTAR models. We first propose a Markov Chain Monte Carlo (MCMC) algorithm for the levels of the time series an...

In this paper, we propose a fully Bayesian approach to the special class of nonlinear time-series models called the logistic smooth transition autoregressive (LSTAR) model. Initially, a Gibbs sampler is proposed for the LSTAR where the lag length, k, is kept fixed. Then, uncertainty about k is taken into account and a novel reversible jump Markov C...

Abstract In this paper we propose a fully Bayesian approach to the special class of nonlinear time series models called the logistic smooth transition autoregressive (LSTAR) model. Initially, a Gibbs sampler is proposed for the LSTAR where the lag length, k, is kept flxed. Then, uncertainty about k is taken into account and a novel reversible jump...