A function-on-function linear quantile regression model, where both the response and predictors consist of random curves, is proposed by extending classical setting into functional data to characterize entire conditional distribution response. In this paper, a partial approach, analog least squares regression, estimate model. covariance function first used extract basis functions. The extracted...

Data integration has become increasingly popular owing to the availability of multiple data sources. This study considered quantile regression estimation when a key covariate had proxies across several datasets. In unified procedure, proposed method incorporates that have both linear and nonlinear relationships with unobserved covariates. The approach allows inference function covariates does n...

Threshold regression models are useful for identifying subgroups with heterogeneous parameters. The conventional threshold split the sample based on a single and observed variable, which enforces point to be equal all of population. In this article, we consider more flexible single-index model in quantile setup, is linear combination predictors. We propose new estimator by smoothing indicator f...

This paper studies the connections among quantile regression, the asymmetric Laplace distribution, and the maximum entropy. We show that the maximum likelihood problem is equivalent to the solution of a maximum entropy problem where we impose moment constraints given by the joint consideration of the mean and median. Using the resulting score functions we develop a maximum entropy quantile regr...

This paper studies high-dimensional parametric quantile regression models, where the dimension of the model increases with the sample size. we focus on the highdimensional low sample size (HDLSS) setting where the number of covariates is allowed to be larger than the sample size. The underlying assumption of the model that allows for a meaningful estimation is the sparseness of the true model. ...

Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for di¤erent predictors to a¤ect di¤erent quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future in‡a...

After its inception in Koenker and Bassett (1978), quantile regression has become an important and widely used technique to study the whole conditional distribution of a response variable and grown into an important tool of applied statistics over the last three decades. In this work, we focus on the variable selection aspect of penalized quantile regression. Under some mild conditions, we demo...

In this paper, we discuss some practical computational issues for quantile regression. We consider the computation from two aspects: estimation and inference. For estimation, we cover three algorithms: simplex, interior point, and smoothing. We describe and compare these algorithms, then discuss implementation of some computing techniques, which include optimization, parallelization, and sparse...

This paper demonstrates that metaheuristic algorithms can provide a useful general framework for estimating both linear and nonlinear econometric models. Two metaheuristic algorithms—firefly and accelerated particle swarm optimization—are employed in the context of several quantile regression models. The algorithms are stable and robust to the choice of starting values and the presence of vario...