Explore the vast range of titles available.

Background Count time series (e.g., daily deaths) are a very common type of data in environmental health research. The series is generally autocorrelated, while the widely used generalized linear model is based on the assumption of independent outcomes. None of the existing methods for modelling parameter-driven count time series can obtain consistent and reliable standard error of parameter estimates, causing potential inflation of type I error rate. Methods We proposed a new maximum significant ρ correction (MSRC) method that utilizes information of significant autocorrelation coefficient ρ estimate within 5 orders by moment estimation. A Monte Carlo simulation was conducted to evaluate and compare the finite sample performance of the MSRC and classical unbiased correction (UB-corrected) method. We demonstrated a real-data analysis for assessing the effect of drunk driving regulations on the incidence of road traffic injuries (RTIs) using MSRC in Shenzhen, China. Moreover, there is no previous paper assessing the time-varying intervention effect and considering autocorrelation based on daily data of RTIs. Results Both methods had a small bias in the regression coefficients. The autocorrelation coefficient estimated by UB-corrected is slightly underestimated at high autocorrelation (≥ 0.6), leading to the inflation of the type I error rate. The new method well controlled the type I error rate when the sample size reached 340. Moreover, the power of MSRC increased with increasing sample size and effect size and decreasing nuisance parameters, and it approached UB-corrected when ρ was small (≤ 0.4), but became more reliable as autocorrelation increased further. The daily data of RTIs exhibited significant autocorrelation after controlling for potential confounding, and therefore the MSRC was preferable to the UB-corrected. The intervention contributed to a decrease in the incidence of RTIs by 8.34% (95% CI, -5.69–20.51%), 45.07% (95% CI, 25.86–59.30%) and 42.94% (95% CI, 9.56–64.00%) at 1, 3 and 5 years after the implementation of the intervention, respectively. Conclusions The proposed MSRC method provides a reliable and consistent approach for modelling parameter-driven time series with autocorrelated count data. It offers improved estimation compared to existing methods. The strict drunk driving regulations can reduce the risk of RTIs.

For a broad class of multilevel models, there exist two well-known competing parameterizations, the centered parameterization (CP) and the non-centered parameterization (NCP), for effective MCMC implementation. Much literature has been devoted to the questions of when to use which and how to compromise between them via partial CP/NCP. This article introduces an alternative strategy for boosting MCMC efficiency via simply interweaving-but not alternating-the two parameterizations. This strategy has the surprising property that failure of both the CP and NCP chains to converge geometrically does not prevent the interweaving algorithm from doing so. It achieves this seemingly magical property by taking advantage of the discordance of the two parameterizations, namely, the sufficiency of CP and the ancillarity of NCP, to substantially reduce the Markovian dependence, especially when the original CP and NCP form a \"beauty and beast\" pair (i.e., when one chain mixes far more rapidly than the other). The ancillarity-sufficiency reformulation of the CP-NCP dichotomy allows us to borrow insight from the well-known Basu's theorem on the independence of (complete) sufficient and ancillary statistics, albeit a Bayesian version of Basu's theorem is currently lacking. To demonstrate the competitiveness and versatility of this ancillarity-sufficiency interweaving strategy (ASIS) for real-world problems, we apply it to fit (1) a Cox process model for detecting changes in source intensity of photon counts observed by the Chandra X-ray telescope from a (candidate) neutron/quark star, which was the problem that motivated the ASIS strategy as it defeated other methods we initially tried; (2) a probit model for predicting latent membranous lupus nephritis; and (3) an interval-censored normal model for studying the lifetime of fluorescent lights. A bevy of open questions are presented, from the mysterious but exceedingly suggestive connections between ASIS and fiducial/structural inferences to nested ASIS for further boosting MCMC efficiency. This article has supplementary material online.

Interrupted time series (ITS)—a quasi-experimental design—is often used to evaluate the effectiveness of a health policy intervention. When the outcome of interest is rare, for example, for certain hospital-acquired infections, a common practice is to focus on aggregated count outcomes. However, analyzing ITS with count outcomes is challenging due to the needs to consider possible overdispersion and to account for serial correlation. In this paper, we compare the performance of three estimation methods, the generalized estimating equation (GEE) method, the generalized linear model (GLM) method, and the composite likelihood (CL) method, to fit parameter-driven time series models with count outcomes, and develop a simulation-based approach to calculate the sample size and power for designing such studies.

Background Porcine reproductive and respiratory syndrome (PRRS) is one of the most economically devastating infectious diseases for the swine industry. A better understanding of the disease dynamics and the transmission pathways under diverse epidemiological scenarios is a key for the successful PRRS control and elimination in endemic settings. In this paper we used a two step parameter-driven (PD) Bayesian approach to model the spatio-temporal dynamics of PRRS and predict the PRRS status on farm in subsequent time periods in an endemic setting in the US. For such purpose we used information from a production system with 124 pig sites that reported 237 PRRS cases from 2012 to 2015 and from which the pig trade network and geographical location of farms (i.e., distance was used as a proxy of airborne transmission) was available. We estimated five PD models with different weights namely: (i) geographical distance weight which contains the inverse distance between each pair of farms in kilometers, (ii) pig trade weight ( PT ji ) which contains the absolute number of pig movements between each pair of farms, (iii) the product between the distance weight and the standardized relative pig trade weight, (iv) the product between the standardized distance weight and the standardized relative pig trade weight, and (v) the product of the distance weight and the pig trade weight. Results The model that included the pig trade weight matrix provided the best fit to model the dynamics of PRRS cases on a 6-month basis from 2012 to 2015 and was able to predict PRRS outbreaks in the subsequent time period with an area under the ROC curve (AUC) of 0.88 and the accuracy of 85% (105/124). Conclusion The result of this study reinforces the importance of pig trade in PRRS transmission in the US. Methods and results of this study may be easily adapted to any production system to characterize the PRRS dynamics under diverse epidemic settings to more timely support decision-making.

The observations in parameter-driven models for time series of counts are generated from latent unobservable processes that characterize the correlation structure. These models result in very complex likelihoods, and even the EM algorithm, which is usually well suited for problems of this type, involves high-dimensional integration. In this article we discuss a Monte Carlo EM (MCEM) algorithm that uses a Markov chain sampling technique in the calculation of the expectation in the E step of the EM algorithm. We propose a stopping criterion for the algorithm and provide rules for selecting the appropriate Monte Carlo sample size. We show that under suitable regularity conditions, an MCEM algorithm will, with high probability, get close to a maximizer of the likelihood of the observed data. We also discuss the asymptotic efficiency of the procedure. We illustrate our Monte Carlo estimation method on a time series involving small counts: the polio incidence time series previously analyzed by Zeger.

Observation and parameter driven models are commonly used in the literature to analyse time series of counts. In this paper, we study the characteristics of a variety of models and point out the main differences and similarities among these procedures, concerning parameter estimation, model fitting and forecasting. Alternatively to the literature, all inference was performed under the Bayesian paradigm. The models are fitted with a latent AR(p) process in the mean, which accounts for autocorrelation in the data. An extensive simulation study shows that the estimates for the covariate parameters are remarkably similar across the different models. However, estimates for autoregressive coefficients and forecasts of future values depend heavily on the underlying process which generates the data. A real data set of bankruptcy in the United States is also analysed.

The INARMA and INGARCH approaches became very popular during the last years if being concerned with the modeling of stationary and ARMA‐like count processes. But also beyond these models, a large number of further models for count time series have been proposed in the literature. Three of these alternatives are presented in the sequel: regression models, Hidden‐Markov models and NDARMA models. The main advantage of regression models is their ability to incorporate covariate information. We discuss common types of generalized linear models, where the conditional mean is “linked” to a linear expression of the available information (past obervations plus covariates). Often a log link is used in practice, whereas the INGARCH models discussed in Chapter 4 might be understood as regression models using the identity link. Hidden‐Markov models are a type of parameter‐driven model for count processes, where the hidden state process constitutes a finite Markov chain. The NDARMA models generate an ARMA‐like dependence structure through some kind of random mixture. These three classes of models are of relevance also in Chapter 7 about the modeling of categorical time series, because they are easily modified to handle such kind of data.

This study presents an innovative framework for leveraging the potential of AI in energy systems through a multidimensional approach. Despite the increasing importance of sustainable energy systems in addressing global climate change, comprehensive frameworks for effectively integrating artificial intelligence (AI) and machine learning (ML) techniques into these systems are lacking. The challenge is to develop an innovative, multidimensional approach that evaluates the feasibility of integrating AI and ML into the energy landscape, to identify the most promising AI and ML techniques for energy systems, and to provide actionable insights for performance enhancements while remaining accessible to a varied audience across disciplines. This study also covers the domains where AI can augment contemporary and future energy systems. It also offers a novel framework without echoing established literature by employing a flexible and multicriteria methodology to rank energy systems based on their AI integration prospects. The research also delineates AI integration processes and technique categorizations for energy systems. The findings provide insight into attainable performance enhancements through AI integration and underscore the most promising AI and ML techniques for energy systems via a pioneering framework. This interdisciplinary research connects AI applications in energy and addresses a varied audience through an accessible methodology.

Improving the performance of renewable energy projects is significant in the global energy transformation process. However, there is no consensus in the literature on which technical indicators are more determinant in these projects, making it difficult for investors and policy makers to make accurate and reliable decisions. To address this research gap, this study aims to optimize renewable energy investment strategies by identifying technical indicators as performance improvement criteria. The novelty of this study lies in the development of an integrated artificial intelligence–based decision-making framework that simultaneously incorporates parameter-driven artificial expert evaluations, dynamic multi-facet fuzzy sets, fuzzy cognitive maps, and principal component ranking optimization. Unlike existing studies, the proposed approach enables dynamic scenario-based adjustment of fuzzy membership parameters, allowing uncertainty to be modeled more realistically under negative, positive, unstable, and natural conditions. This integrated structure provides a more adaptive and data-driven prioritization of technical indicators compared to conventional fuzzy or multi-criteria models. The findings reveal that scalability and ease of maintenance are the most critical factors for enhancing technical performance in renewable energy projects. Accordingly, focusing on easy-to-service microgrids and maximizing lifecycle performance emerge as the most effective investment strategies.