Over the past decade, traffic heteroscedasticity has been investigated with the primary purpose of generating prediction intervals around point forecasts constructed usually by short-term traffic ...

Abstract This paper considers quantile regression for a wide class of time series models including autoregressive and moving average (ARMA) models with asymmetric generalized autoregressive ...

For the usual regression model without replication, we provide a diagnostic test for heteroscedasticity based on the score statistic. A graphical procedure to complement the score test is also ...

Learn how to detect, diagnose, and handle heteroscedasticity, a common problem in regression analysis, using some statistical modeling techniques and R.

Discover how heteroscedasticity can influence your regression analysis and why it's vital to address it for accurate data analytics insights.

In statistics, heteroskedasticity happens when the standard deviations of a variable, monitored over a specific amount of time, are nonconstant.

Generalised autoregressive conditional heteroscedasticity (Garch) Original research Forecasting the Volatility Index with a realized measure, volatility components and dynamic jumps The authors put ...

Spot electricity prices are found to be heteroscedastic in the literature. In this paper I analyze the sources of heteroscedasticity. The heteroscedasticity is measured with the autocorrelation ...

In conclusion, heteroscedasticity was frequently observed in the PRS-based prediction models of quantitative traits, and the accuracy of the predictive model may differ according to PRS values.

The Autoregressive Conditional Heteroscedasticity (ARCH) model is a statistical tool used to analyze and forecast volatility in time series data, particularly in financial markets.