We propose a new family of error distributions for model-based quantile regression, which is constructed through a structured mixture of normal distributions. The construction enables fixing specific percentiles of the distribution while, at the same time, allowing for varying mode, skewness and tail behavior. It thus overcomes the severe limitation of the asymmetric Laplace distribution – the ...

background: low birth weight is one of the key indicators to assess the health of infants, and appropriate birth weight is one of the most important goals of any health system which also reflects the quality of prenatal care. objectives: the present research aimed to study some of the factors associated with low birth weight using quantile regression analysis. methods: a cross-sectional study w...

Quantile regression is a statistical technique intended to estimate, and conduct inference about the conditional quantile functions. Just as the classical linear regression methods estimate models for conditional mean function, quantile regression offers a mechanism for estimating models for conditional median function, and the full range of other conditional quantile functions. In this paper d...

To date the literature on quantile regression and least absolute deviation regression has assumed either explicitly or implicitly that the conditional quantile regression model is correctly specified. When the model is misspecified, confidence intervals and hypothesis tests based on the conventional covariance matrix are invalid. Although misspecification is a generic phenomenon and correct spe...

In this paper, we explore the use of M-quantile regression and M-quantile coefficients to detect statistical differences between temporal curves that belong to different experimental conditions. In particular, we consider the application of temporal gene expression data. Here, the aim is to detect genes whose temporal expression is significantly different across a number of biological condition...

We introduce the local composite quantile regression (LCQR) to causal inference in discontinuity (RD) designs. Kai, Li and Zou study efficiency property of LCQR, while we show that its nice boundary performance translates accurate estimation treatment effects RD under a variety data generating processes. Moreover, propose bias-corrected standard error-adjusted t-test for inference, which leads ...

An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method and an updating procedure are combined into a new algorithm for time-adaptive quantile regression,...