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 ...

This manuscript considers regression models for generalized, multilevel functional responses: functions are generalized in that they follow an exponential family distribution and multilevel in that ...

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 ...

Patient factors such as age, race or ethnicity, body mass index, congestive heart failure, cancer, amputation, functional status, hemoglobin and prior nephrology care are used in the regression model.