Quantile regression models the conditional quantile of a response variable. Compared to least squares, which focuses on the conditional mean, it provides a more complete picture of the conditional distribution. Median regression, a special case of quantile regression, offers a robust alternative to least squares methods. Common regression assumptions are that there is a linear relationship between the covariates, there is no missing data and the sample size is larger than the number of covariates. In this dissertation we examine how to use quantile regression models when these assumptions do not hold. In all settings we examine the issue of variable selection and present methods that have the property of model selection consistency, that is, if the true model is one the candidate models, then these methods select the true model with probability approaching one as the sample size increases.We consider partial linear models to relax the assumption that there is a linear relationship between the covariates. Partial linear models assume some covariates have a linear relationship with the response while other covariates have an unknown non-linear relationship. These models provide the flexibility of non-parametric methods while having ease of interpretation for the targeted parametric components. Additive partial linear models assume an additive form between the non-linear covariates, which allows for a flexible model that avoids the ``curse of dimensionality". We examine additive partial linear quantile regression models using basis splines to model the non-linear relationships.In practice missing data is a common problem and estimates can be biased if observations with missing data are dropped from the analysis. Imputation is a popular approach to handle missing data, but imputation methods typically require distributional assumptions. An advantage of quantile regression is it does not require any distributional assumptions of the response or the covariates. To remain in a distribution free setting a different approach is needed. We use a weighted objective function that provides more weight to observations that are representative of subjects that are likely to have missing data. This approach is analyzed for both the linear and additive partial linear setting, while considering model selection for the linear covariates. In mean regression analysis, detecting outliers and checking for non-constant variance are standard model-checking steps. With high-dimensional data, checking these conditions becomes increasingly cumbersome. Quantile regression offers an alternative that is robust to outliers in the Y direction and directly models heteroscedastic behavior. Penalized quantile regression is considered to accommodate models where the number of covariates is larger than the sample size. The additive partial linear model is extended to the high-dimensional case. We consider the setting where the number of linear covariates increases with the sample size, but the number of non-linear covariates remains fixed. To create a sparse model we compare the LASSO and SCAD penalties for the linear components.