linear regressionGI cancerslength of hospitalizationlt;pgt;lt;stronggt;Objectiveslt;/stronggt;: The aim of this study is to compare linear regression and quantile regression to analyze the predictors of duration of staying in hospital for patients with GI cancers.lt;/pgt;lt;pgt;lt;stronggt;...
Linear Hypothesis: Regression (Quantile)Linear Hypothesis: Regression (Quantile)Classical least squares regression may be viewed as a natural way of extending the idea of estimating an unconditional mean parameter to the problem of estimating conditional mean; the crucial link is the formulation of an...
distributional regressionfunctional Delta-methodasymptotic relative efficiencylinear location modellocation-scale modelsGiven a scalar random variable Y and a random vector X defined on the same probability space, the conditional distribution of Y given X can be represented by either the conditional ...
Shallow Learning—Linear Regression Regression(回归):通过训练输出一个数值。(训练是指寻找合适的function的过程) eg: 股票预测,无人车预测驾驶角度,购买可能性预测 step1:Model 定义一系列可能满足条件的function set Linear Model:注意是指function中的参数跟结果满足线性关系 step2:Goodness of function 用一个...
Type of model to fit. Options are ‘lm’ (linear regression), ‘qr’ (quantile regression), ‘logit’ (logistic regression for binary classification, output binary classification), ‘logit_proba’ (logistic regression for binary classification, output probability) and ‘logit_raw’ (logistic regress...
Bayesian quantile regressionPartial linear single-index modelPower consumption modelReversible jump Markov Chain Monte CarloVariable selectionWe advocate linear regression by modeling the error term through a finite mixture of asymmetric Laplace distributions (ALDs). The model expands the flexibility of ...
This study aims to address the problem of high-dimensional quantile regression within the context of transfer learning and investigates the impact of incorporating linear constraints. In the case of known transferable sources, a two-step transfer learning algorithm is proposed in this study. To ...
Quantile regressionPartially linear modelEmpirical likelihoodMissing data62G0862G20In this paper, we consider the confidence interval construction for partially linear quantile regression models with missing response at random. We propose an imputation based empirical likelihood met...
In this article, we consider quantile regression method for partially linear varying coefficient models for semiparametric time series modeling. We propose estimation methods based on general series estimation. We establish convergence rates of the estimator and the root-n asymptotic normality of the fini...
In this paper, we propose the double-penalized quantile regression estimators in partially linear models. An iterative algorithm is proposed for solving the proposed optimization problem. Some numerical examples illustrate that the finite sample performances of proposed method perform better than the least...