Linear Regression Cost function in Machine Learning is "error" representation between actual value and model predictions. To minimize the error, we need to minimize the Linear Regression Cost ...

We propose a value-at-risk model for foreign exchange risk that outperforms traditional benchmark models in- and out-of-sample. The quantile regression model is forward-looking in nature, as directly ...

The ARIMA procedure provides the identification, parameter estimation, and forecasting of autoregressive integrated moving average (Box-Jenkins) models, seasonal ARIMA models, transfer function models ...

Panel count data are commonly encountered in analysis of recurrent events where the exact event times are unobserved. To accommodate the potential non-linear covariate effect, we consider a ...

Joint mean-covariance regression modeling with unconstrained parametrization for continuous longitudinal data has provided statisticians and practitioners with a powerful analytical device. How to ...