In energy demand forecasting, the objective function is often symmetric, implying that over-
prediction errors and under-prediction errors have the same consequences. In practice …

This paper proposes a linear quantile regression analysis method for longitudinal data that
combines the between-and within-subject estimating functions, which incorporates the …

A new technique based on Bayesian quantile regression that models the dependence of a
quantile of one variable on the values of another using a natural cubic spline is presented …

We develop two empirical likelihood-based inference procedures for longitudinal data under
the framework of quantile regression. The proposed methods avoid estimating the unknown …

The inquiries to return predictability are traditionally limited to conditional mean, while
literature on portfolio selection is replete with moment-based analysis with up to the fourth …

Statistical inference in censored quantile regression is challenging, partly due to the
unsmoothness of the quantile score function. A new procedure is developed to estimate the …

Finite-difference approximations are widely used in empirical work to evaluate derivatives of
estimated functions. For instance, many standard optimization routines rely on finite …

Inference and prediction in quantile regression for longitudinal data are challenging without
parametric distributional assumptions. We propose a new semiparametric approach that …

Development in methodology on longitudinal data is fast. Currently, there are a lack of
intermediate/advanced level textbooks which introduce students and practicing statisticians …

With a rapid decline in cost of battery energy storage, a battery system plays an increasingly
important role in managing imbalance between ordering and consumption in the electricity …