Quantile regression
Quantile Regression Forests
... conditional quantile approximations, loss func- tion (3) is used in conjunction with 5-fold ...conditional quantile function, as discussed ...between quantile regression forests and the other ...
17
Objective Bayesian analysis on the quantile regression
... the quantile regression, ranging from the frequentist approaches to the Bayesian ...the regression coefficients at some fixed quantile ...the quantile regression models, to ...
99
Quantile regression methods in economics and finance
... applied quantile regression in a financial ...binary quantile regression approach, a hybrid bankruptcy prediction model with dynamic loadings for both the accounting-ratio-based and ...
162
Penalized single-index quantile regression
... The majority of known estimation approaches for model (1) were constructed on either least squares or likelihood based methods. Thus, these approaches are expected to be sensitive to outliers. In contrast to the stated ...
19
Censored quantile regression with auxiliary information
... a regression calibration based approach to the censored quantile regression ...the regression calibration method with the help of the auxiliary covariate and other available ...censored ...
155
Quantile regression with group lasso for classification
... binary quantile regression models with the use of a group lasso ...binary regression and offers an alternative to the mean-based logistic regression model with group lasso penalty (Meier et al ...
15
Bayesian lasso binary quantile regression
... binary quantile regression, we refer to Manski (1975, 1985), Kordas (2006) and Benoit and Van den Poel (2012) for an ...because quantile regression is able to accommodate for non-normal ...
14
High quantile regression for extreme events
... mean of daily snowfall y for a given maximum temperature x. But, the least squares curve does not provide information about extreme heavy snowfalls that may cause damage. The quantile regression method will ...
20
Penalized Flexible Bayesian Quantile Regression
... Bayesian quantile regression model for independent and clustered ...the quantile of interest) as with other approaches (personal communication with ...
14
Bayesian Quantile Regression in Biostatistical Applications.
... for quantile regression with these necessary ...group quantile effects while accounting for within-subject dependence, enabling inference at the population and subject ...to quantile function ...
127
Semiparametric quantile regression with random censoring
... linear regression model, quantile regression (Bassett & Koenker 1978, Koenker & Bassett 1978) has been widely used in economics, …nance, biostatistics and medical statistics - see Koenker ...
38
Quantile regression : a penalization approach
... In recent years, high-dimensional data in which the number of covari- ates p is larger than the number of observations n (p n), has become increasingly common. This problem can be found in many different areas like ...
32
Poverty and Intelligence: Evidence Using Quantile Regression
... The findings presented in Table 2 entail OLS, LAD and QR estimates. OLS estimates provide a baseline of mean effects and we compare these to estimates of LAD and separate quantiles in the conditional distributions. In ...
10
Demand for International Reserves: A Quantile Regression Approach
... the quantile regression results when four different measures of reserve holdings are ...the quantile regression point estimates for θ in increments of ...one quantile to another, ...
21
Efficient Estimation of an Additive Quantile Regression Model
... additive quantile regression ...polynomial quantile smoothing for each additive component with the other additive components replaced by the corresponding estimates from the first ...additive ...
36
Innovation and market value: A quantile regression analysis
... a quantile regression approach avoids the restrictive assumption that the error terms are identically distributed at all points of the conditional ...
10
A quantile regression approach to bank efficiency measurement
... Lately, a third approach was proposed in the literature, namely Quantile regression analysis. This technique has been frequently employed in the econometrics literature; however, there are only a few ...
21
Quantile Regression for Repeated Responses Measured with Error.
... ditional quantile curves of usual sodium intake as a function of age for two domains in the ...These quantile curves clearly offer a broader perspective about the usual sodium intake patterns across ...a ...
107
On High-Dimensional Misspecified Quantile Regression
... of quantile regression and least squares estimators after model selection via maximal inequalities that hold uniformly over collections of models; and Kuchibhotla et ...the quantile regression ...
159
Bayesian empirical likelihood for quantile regression
... for quantile regression, we need to work with the quantile estimating equations that involve discontinuous functions, so direct local expansions used in the EL literature cannot be ...
89