Results 91 to 100 of about 113,685 (321)

Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach

Journal of Forecasting, EarlyView.
ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch, Candace Berrett, Mason Ferlic, Kimberly F. Sellers   +3 more
wiley   +1 more source

Bayesian Estimation of Spatial Lagged Panel Quantile Regression Model

Applied Sciences
This paper proposes a Bayesian estimation method for spatial lagged panel quantile models. The proposed model simultaneously considers spatial lag effects of the dependent variable and the quantile regression framework, enabling effective capture of ...
Man Zhao, Rushan Huang, Hanfang Li, Youxi Luo, Qiming Liu   +4 more
doaj   +1 more source

Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression

Promet (Zagreb), 2020
With the popularization of intelligent transportation system and Internet of vehicles, the traffic flow data on the urban road network can be more easily obtained in large quantities. This provides data support for shortterm traffic flow prediction based
Jing Luo
doaj   +1 more source

The Impact of the 2016 EU Audit Reforms, Oversight, and Corruption on Earnings Management: Evidence From European Banks Using a Dynamic Panel Approach

International Journal of Finance &Economics, EarlyView.
ABSTRACT This study investigates earnings management in European banks in the context of the 2016 EU audit directive. Using a dynamic panel of 134 banks over 2012–2023, we apply two‐step System‐GMM estimators with three profitability measures—Earnings Before Provisions and Taxes (EBPT), Return on Assets (ROA), and Return on Equity (ROE).
Maria Christofidou, Epameinondas Katsikas, Dimitrios Koufopoulos, Konstantinos Spanos   +3 more
wiley   +1 more source

Variational Bayesian Quantile Regression with Non-Ignorable Missing Response Data

Axioms
For non-ignorable missing response variables, the mechanism of whether the response variable is missing can be modeled through logistic regression. In Bayesian computation, the lack of a conjugate prior for the logistic function poses a significant ...
Juanjuan Zhang, Weixian Wang, Maozai Tian   +2 more
doaj   +1 more source

Retirement Consumption Puzzle in Malaysia: Evidence from Bayesian Quantile Regression Model

Journal of Probability and Statistics, 2019
The objective of this study is to use the Bayesian quantile regression for studying the retirement consumption puzzle, which is defined as the drop in consumption upon retirement, using the cross-sectional data of the Malaysian Household Expenditure ...
Ros Idayuwati Alaudin, Noriszura Ismail, Zaidi Isa   +2 more
doaj   +1 more source

Technological Evolution in Fintech: A Decadal Scientometric and Systematic Review of Developments and Criticisms

International Journal of Finance &Economics, EarlyView.
ABSTRACT This study aims to classify pivotal fintech innovations and explore the prospects and pitfalls associated with emerging fintech services extensively discussed in the literature. We conducted a multistage systematic review of research published on fintech over the past decade from a technological perspective. Using the Preferred Reporting Items
Muhammad Imran Qureshi, Nohman Khan
wiley   +1 more source

From Reactive to Proactive Volatility Modeling With Hemisphere Neural Networks

Journal of Applied Econometrics, EarlyView.
ABSTRACT We revisit maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context.
Philippe Goulet Coulombe, Mikael Frenette, Karin Klieber   +2 more
wiley   +1 more source

Bayesian Quantile Regression for Partial Functional Linear Spatial Autoregressive Model

Axioms
When performing Bayesian modeling on functional data, the assumption of normality is often made on the model error and thus the results may be sensitive to outliers and/or heavy tailed data.
Dengke Xu, Shiqi Ke, Jun Dong, Ruiqin Tian   +3 more
doaj   +1 more source

Post‐Processed CMIP6 Climate Projections for Hydro‐Environmental Risk Assessment in the Middle East and Central Asia

International Journal of Climatology, EarlyView.
Estimating water resources is important for regional climate impact analysis and risk estimation. The Middle East and Central Asia have largely reached the limit of sustainably usable water across their river basins and ecosystems. Strategies designed to mitigate environmental risks require a reliable estimation of water availability trends.
Paolo Reggiani, Amal Talbi, Oleksiy Boyko, Poolad Karimi, Ezio Todini   +4 more
wiley   +1 more source