Function-on-scalar regressionImage analysisMinimax rate of convergenceQuantile regressionReproducing kernel Hilbert spaceThis paper develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar ...
scalable inference in functional linear regression with streaming data. (2023) arxiv preprint arxiv:2302.02457 yang, h., baladandayuthapani, v., rao, a.u., morris, j.s.: quantile function on scalar regression analysis for distributional data. j. am. stat. assoc. 115 (529), 90–106 (...
beta分布的概率密度的matlab代码分布数据标量回归分析中的分位数函数 作者贡献清单表 数据 抽象的 多形胶质母细胞瘤(GBM)是最常见和最具侵害性的癌症,始于大脑。 大多数GBM诊断是通过医学成像(例如磁共振成像(MRI))进行的,其中MRI提供了广泛的高分辨率图像对比度,可作为临床决策或GBM研究中肿瘤进展的指标。 通常起...
We propose a function-on-function regression model that predicts a functional response by both a nonlinear dynamic effect of a functional predictor and a linear concurrent effect of another functional predictor. The nonlinear dynamic effect is characterized by taking an integral of a time-dependent tw...
Bayesian Multiple Quantile Regression for Linear Models Using a Score Likelihood* 2021, Bayesian Analysis Function-on-scalar quantile regression with application to mass spectrometry proteomics data 2020, Annals of Applied Statistics BayesQR: A bayesian approach to quantile regression 2017, Journal of Stati...
the superlevel set is a function of conditioning variables much like in quantile regression. We show that conditional superlevel sets have favorable mathematical and intuitive features, and support a clear probabilistic interpretation. We derive the superlevel sets for a conditional or marginal density...
Quantile Regression with Random Forests January 1, 20244 Comments In my December 22 blog, I first introduced the classic parametric quantile regression (QR) concept. I then showed how one could use theqeMLpackage to perform quantile regression nonparametrically, using the package’sqeKNNfunction fo...
The Bayesian model has two regression coefficient parameters, {wage:age} and {wage: cons}, and a positive scalar parameter, {sigma2}, representing the variance of the error term. The model sum- mary shows the default priors used for the model parameters: normal(0, 10000) for the ...
, n . Then, we fit a function-on-scalar regression as follows: 13 Machine Learning (2024) 113:2765–2794 2773 p ∑ ̃ i(t) = 0(t) + j(t)xij + i(t), i = 1, … , n. j=1 Observe that this model has functional coefficients depending on t, where 0(t) is the func-...
In the contemporary global landscape, understanding the nexus between financial inclusion and natural resource abundance is crucial, especially for resource-rich nations. This study uses diagnostic tests and method of moments quantile regression to exami