18,390 research outputs found
Recommended from our members
Dynamic Bayesian smooth transition autoregressive models applied to hourly electricity load in southern Brazil
Dynamic Bayesian Smooth Transition Autoregressive (DBSTAR) models are proposed for nonlinear autoregressive time series processes as alternative to both the classical Smooth Transition Autoregressive (STAR) models of Chan and Tong (1986) and the Bayesian Simulation STAR (BSTAR) models of Lopes and Salazar (2005). Unlike those, DBSTAR models are sequential polynomial dynamic analytical models suitable for inherently non-stationary time series with non-linear characteristics such as asymmetric cycles. As they are analytical, they also avoid potential computational problems associated with BSTAR models and allow fast sequential estimation of parameters.
Two types of DBSTAR models are defined here based on the method adopted to approximate the transition function of their autoregressive components, namely the Taylor and the B-splines DBSTAR models. A harmonic version of those models, that accounted for the cyclical component explicitly in a flexible yet parsimonious way, were applied to the well-known series of annual Canadian lynx trappings and showed improved fitting when compared to both the classical STAR and the BSTAR models. Another application to a long series of hourly electricity loading in southern Brazil, covering the period of the South-African Football World Cup in June 2010, illustrates the short-term forecasting accuracy of fast computing harmonic DBSTAR models that account for various characteristics such as periodic behaviour (both within-the-day and within-the-week) and average temperature
Scalable Bayesian model averaging through local information propagation
We show that a probabilistic version of the classical forward-stepwise
variable inclusion procedure can serve as a general data-augmentation scheme
for model space distributions in (generalized) linear models. This latent
variable representation takes the form of a Markov process, thereby allowing
information propagation algorithms to be applied for sampling from model space
posteriors. In particular, we propose a sequential Monte Carlo method for
achieving effective unbiased Bayesian model averaging in high-dimensional
problems, utilizing proposal distributions constructed using local information
propagation. We illustrate our method---called LIPS for local information
propagation based sampling---through real and simulated examples with
dimensionality ranging from 15 to 1,000, and compare its performance in
estimating posterior inclusion probabilities and in out-of-sample prediction to
those of several other methods---namely, MCMC, BAS, iBMA, and LASSO. In
addition, we show that the latent variable representation can also serve as a
modeling tool for specifying model space priors that account for knowledge
regarding model complexity and conditional inclusion relationships
Why do we need to employ Bayesian statistics and how can we employ it in studies of moral education?: With practical guidelines to use JASP for educators and researchers
ABSTRACTIn this article, we discuss the benefits of Bayesian statistics and how to utilize them in studies of moral education. To demonstrate concrete examples of the applications of Bayesian statistics to studies of moral education, we reanalyzed two data sets previously collected: one small data set collected from a moral educational intervention experiment, and one big data set from a large-scale Defining Issues Test-2 survey. The results suggest that Bayesian analysis of data sets collected from moral educational studies can provide additional useful statistical information, particularly that associated with the strength of evidence supporting alternative hypotheses, which has not been provided by the classical frequentist approach focusing on P-values. Finally, we introduce several practical guidelines pertaining to how to utilize Bayesian statistics, including the utilization of newly developed free statistical software, Jeffrey’s Amazing Statistics Program, and thresholding based on Bayes Factors, to scholars in the field of moral education
- …