Directional Regression Quantile Computation
Quantile regression models, as an important tool in practice, can describe effects of risk factors on the entire conditional distribution of the response variable with its estimates robust to outliers. However, there is few discussion on quantile regression for longitudinal data with both missing ...
Extreme quantile regression provides estimates of conditional quantiles outside the range of the data. Classical quantile regression performs poorly in suc
Bayesian tobit quantile regression using g- prior distribution with ridge parameter. Computational Statistics & Data Analysis, revised manuscript.Bayesian Tobit quantile regression using g-prior distribution with ridge parameter[J] . Rahim Alhamzawi,Keming Yu.Journal of Statistical Computation and Simulation...
Computation • All computations involving data are performed in floating-point; therefore, all data provided must havetype/realconsand all returned solutions are floating-point, even if the problem is specified with exact values. • By default, all computations involving random variables are perfo...
(2017). Exact computation of gmm estimators for instrumental variable quantile regression models. arXiv preprint arXiv:1703.09382.Chen, L.-Y., Lee, S., 2017. Exact computation of GMM estimators for instrumental variable quantile regression models, working paper, available at https://arxiv.org/...
Quantile regression models have become a widely used statistical tool in genetics and in the omics fields because they can provide a rich description of th
ned by Qy (τjx ) = minfηjP(y ηjx ) τg. 0.6 0.5 0.4 0.3 0.2 0.1 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 y Bernoulli probability mass function with Pr (y = 1) = 0.6 3 3. Basics of quantile regression Quantile regression estimates Qy (τjx ). Throughout we assume ...
For the quantile part, one can use L1-penalized quantile regression45with a penalty proportional to sparsity (e.g.,\(\lambda=\frac{2p}{{n}_{+{vs}}}\)or\(\frac{2p}{{{\log }}(n_{+{vs}})}\)). Like other LASSO methods, this makes the computation feasible when the non-zero co...
Tarr, G. (2012). Small sample performance of quantile regression confidence intervals. Journal of Statistical Computation and Simulation, 82(1), 81-94. https://doi.org/10.1080/00949655.2010.527844G Tarr 2012 Small sample performance of quantile regression confidence intervals J. Stat. Comput. ...