7 citations · 15 across the 19 of their papers we have counts for
23 papers
Scalable likelihood-based inference for limited dependent variable models
David T. Frazier, Ruben Loaiza-Maya, Didier Nibbering
Limited dependent variable models are central to empirical economics, but likelihood-based inference is infeasible when likelihoods involve high-dimensional integration over latent…
A Multinomial Probit Model for Asymmetric Choice Responses
Cash Looi, Ruben Loaiza-Maya, Didier Nibbering
Standard multinomial probit (MNP) models specify symmetric latent utility distributions, implying that choice probabilities respond symmetrically to positive and negative covariate…
Conjugating Variational Inference for Large Mixed Multinomial Logit Models and Consumer Choice
Weiben Zhang, Ruben Loaiza-Maya, Michael Stanley Smith +1
Heterogeneity in multinomial choice data is often accounted for using logit models with random coefficients. Such models are called "mixed", but they can be difficult to estimate f…
Robustifying Approximate Bayesian Computation
Chaya Weerasinghe, David T. Frazier, Ruben Loaiza-Maya +1
Approximate Bayesian computation (ABC) is one of the most popular "likelihood-free" methods. These methods have been applied in a wide range of fields by providing solutions to int…
ABC-based Forecasting in State Space Models
Chaya Weerasinghe, Ruben Loaiza-Maya, Gael M. Martin +1
Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in som…
Hybrid unadjusted Langevin methods for high-dimensional latent variable models
Ruben Loaiza-Maya, Didier Nibbering, Dan Zhu
The exact estimation of latent variable models with big data is known to be challenging. The latents have to be integrated out numerically, and the dimension of the latent variable…
Optimal probabilistic forecasts for risk management
Yuru Sun, Worapree Maneesoonthorn, Ruben Loaiza-Maya +1
This paper explores the implications of producing forecast distributions that are optimized according to scoring rules that are relevant to financial risk management. We assess the…
Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models
Weiben Zhang, Michael Stanley Smith, Worapree Maneesoonthorn +1
Stochastic models with global parameters and latent variables are common, and for which variational inference (VI) is popular. However, existing methods are often either slow or in…
Bayesian Forecasting in Economics and Finance: A Modern Review
Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn +6
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem…
Efficient variational approximations for state space models
Rubén Loaiza-Maya, Didier Nibbering
Variational Bayes methods are a potential scalable estimation approach for state space models. However, existing methods are inaccurate or computationally infeasible for many state…
Bayesian Neural Network Versus Ex-Post Calibration For Prediction Uncertainty
Satya Borgohain, Klaus Ackermann, Ruben Loaiza-Maya
Probabilistic predictions from neural networks which account for predictive uncertainty during classification is crucial in many real-world and high-impact decision making settings…
Fast variational Bayes methods for multinomial probit models
Rubén Loaiza-Maya, Didier Nibbering
The multinomial probit model is often used to analyze choice behaviour. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which l…
Implicit copula variational inference
Michael Stanley Smith, Rubén Loaiza-Maya
Key to effective generic, or "black-box", variational inference is the selection of an approximation to the target density that balances accuracy and speed. Copula models are promi…
Variational Bayes in State Space Models: Inferential and Predictive Accuracy
David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin
Using theoretical and numerical results, we document the accuracy of commonly applied variational Bayes methods across a range of state space models. The results demonstrate that,…
Loss-Based Variational Bayes Prediction
David T. Frazier, Ruben Loaiza-Maya, Gael M. Martin +1
We propose a new approach to Bayesian prediction that caters for models with a large number of parameters and is robust to model misspecification. Given a class of high-dimensional…
Optimal probabilistic forecasts: When do they work?
Gael M. Martin, Rubén Loaiza-Maya, David T. Frazier +2
Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Here…
Robust Approximate Bayesian Computation: An Adjustment Approach
David T. Frazier, Christopher Drovandi, Ruben Loaiza-Maya
We propose a novel approach to approximate Bayesian computation (ABC) that seeks to cater for possible misspecification of the assumed model. This new approach can be equally appli…
Scalable Bayesian estimation in the multinomial probit model
Ruben Loaiza-Maya, Didier Nibbering
The multinomial probit model is a popular tool for analyzing choice behaviour as it allows for correlation between choice alternatives. Because current model specifications employ…
Fast and Accurate Variational Inference for Models with Many Latent Variables
Rubén Loaiza-Maya, Michael Stanley Smith, David J. Nott +1
Models with a large number of latent variables are often used to fully utilize the information in big or complex data. However, they can be difficult to estimate using standard app…
Focused Bayesian Prediction
Ruben Loaiza-Maya, Gael M. Martin, David T. Frazier
We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defi…
High-dimensional copula variational approximation through transformation
Michael Stanley Smith, Ruben Loaiza-Maya, David J. Nott
Variational methods are attractive for computing Bayesian inference for highly parametrized models and large datasets where exact inference is impractical. They approximate a targe…
Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series
Ruben Loaiza-Maya, Michael Stanley Smith
We propose a new variational Bayes estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational…
Time Series Copulas for Heteroskedastic Data
Rubén Loaiza-Maya, Michael S. Smith, Worapree Maneesoonthorn
We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We develop our copula for first order Markov series, and extend it to higher…