Estimation of conditional quantiles at very high or low tails is of interest in numerous applications. Quantile regression provides a convenient and natural way of quantifying the impact of covariates at different quantiles of a response distribution. However, high tails are often associated...

The estimation of extreme conditional quantiles is an important issue in numerous disciplines. Quantile regression (QR) provides a natural way to capture the covariate effects at different tails of the response distribution. However, without any distributional assumptions, estimation from...

Consider a structural break autoregressive (SBAR) process <disp-formula id="UM0001"> <graphic xmlns:xlink="http://www.w3.org/1999/xlink" position="float" orientation="portrait" xlink:href="uasa_a_866566_um0001.gif"/> </disp-formula>where <inline-formula> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="uasa_a_866566_ilm0001.gif"/> </inline-formula> <italic>j</italic> = 1, ..., <italic>m</italic> + 1, {<italic>t</italic> ₁, ..., <italic>t_m </italic>} are change-points, 1 = <italic>t</italic> ₀ <italic>t</italic> ₁ ⋅⋅⋅ <italic>t</italic> _{<italic>m</italic> + 1} = <italic>n</italic> + 1, σ( · ) is a measurable function on <inline-formula> <inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="uasa_a_866566_ilm0002.gif"/> </inline-formula>, and {ϵ_{<italic>t</italic>}} are white noise with unit variance. In practice, the number of change-points...</italic>