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 ...
We propose a novel and robust online function-on-scalar regression technique via geometric median to learn associations between functional responses and sc
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...
The function of the function namedfunctionis to create functions! Now, going back tomakeQFtnabove, it creates a function object (the call to quantile), and returns that object, just as withfabove, but the key point is that here the value ofqwill be “baked in” to that object. ...
See the Linear regression: A case of informative default priors example below. All positive scalar parameters, such as the variance parameters in bayes: regress and bayes: tobit, are assigned an inverse-gamma prior with shape parameter and scale parameter , InvGamma( , ). The default values ...
Function-on-scalar regression models (Reiss et al., 2010; Goldsmith et al., 2015) provide powerful tools to explore and understand previous data and predict curves of attendance as a function of time, relative to the beginning of the event, for future events at the stadium. This, ...
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