Conditional mode regression: Application to functional time series prediction

用于多变量分类响应回归的条件概率张量分解

Conditional probability tensor decompositions for multivariate categorical response regression

针对多变量分类响应回归中响应、类别及预测维度均较大的问题，提出基于函数型概率张量分解的建模方法，推导可扩展的惩罚EM算法拟合，经模拟和基因功能建模验证性能良好。

Bayesian Mode Regression

Like mean, quantile and variance, mode is also an important measure of central tendency and data summary. Many practical questions often focus on "Which element (gene or file or signal) occurs most often or is the most typical among all elements in a network?". In such cases mode regression provides a convenient summary of how the regressors affect the conditional mode and is totally different from other regression models based on conditional mean or conditional quantile or conditional variance. Some inference methods have been used for mode regression but none of them from the Bayesian perspe…

用于英超联赛进球依存关系的贝叶斯双变量条件泊松回归模型

A Bayesian bivariate conditional Poisson regression for goal dependence in the English Premier League

针对传统足球得分模型的局限，本文提出贝叶斯双变量条件泊松回归模型，分析英超联赛主客场进球依存关系，发现二者负相关且上座率对主客场得分的影响不对称。

Linear Regression Models in Epidemiology

The paper proposes to analyze epidemiological data using regression models which enable subject-matter (epidemiological) interpretation of such data whether with uncorrelated or correlated predictors. To this end, response functions should include not only terms linear in predictors but also higher order ones (e.g. quadratic and cross terms). For epidemiological interpretation of a regression model, the suggestion is to construct conditional functions derived from the general regression function with the values of all predictor variables held fixed excepting one predictor. Unlike the conventio…

符号量化回归用于条件量化可解释性预测

Symbolic Quantile Regression for the Interpretable Prediction of Conditional Quantiles

本文提出了一种符号量化回归方法，用于预测条件量化并解释预测变量对结果的影响，通过在航空燃料使用案例中比较预测极值和中央结果的模型，展示了SQR在高风险应用中的有效性。

Robust Function-on-Function Regression

Functional linear regression is a widely used approach to model functional responses with respect to functional inputs. However, classical functional linear regression models can be severely affected by outliers. We therefore introduce a Fisher-consistent robust functional linear regression model that is able to effectively fit data in the presence of outliers. The model is built using robust functional principal component and least squares regression estimators. The performance of the functional linear regression model depends on the number of principal components used. We therefore introduce…

修正线性单元回归

Rectified Linear Unit Regression

提出一种名为修正线性单元（ReLU）回归的方法，通过将ReLU变换后的结果投影到协变量上，直接估计条件结果分布的积分泛函，并建立其渐近分布和推断方法，扩展了经验研究中可用的分布参数集。

条件不平等曲线与测度的估计：基于条件分位数函数的估计方法

Estimation of conditional inequality curves and measures via estimating the conditional quantile function

本文扩展经典不平等测度至条件情形，提出基于保序回归的无交叉条件分位数估计法，证明一致性并模拟验证，应用于年龄组薪资不平等分析。

CATE Lasso：基于高维线性回归的条件平均处理效应估计

CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression

本文针对高维非稀疏参数下条件平均处理效应（CATE）的一致估计问题，提出基于隐式稀疏性假设的 CATE Lasso 方法，并证明其一致性且通过模拟验证有效性。