This study proposes an important new approach to analyzing cell-count data, which are often undersampled and cannot be accurately assessed using traditional statistical methods. The case studies ...

Predicting performance for large-scale industrial systems—like Google’s Borg compute clusters—has traditionally required extensive domain-specific feature engineering and tabular data representations, ...

As one of the important statistical methods, quantile regression (QR) extends traditional regression analysis. In QR, various quantiles of the response variable are modeled as linear functions of the ...

Abstract: In surveys conducted by Badan Pusat Statistik (BPS), such as SUSENAS, many households do not allocate expenditures for certain types of consumer goods. This causes a lot of censored data. An ...

Sudden reductions in crop yield (i.e., yield shocks) severely disrupt the food supply, intensify food insecurity, depress farmers' welfare, and worsen economic conditions in a country. Here, we study ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...

This paper develops a distribution-on-scalar single-index quantile regression modeling framework to investigate the relationship between cancer imaging responses and scalar covariates of interest ...

This new narrative is conciliatory, but only thinly linked to the presented statistical evidence. The existence and location ($100,000) of a threshold was not estimated in Killingsworth’s data but was ...

Bitcoin’s (BTC) strong weekly return of 9.84% exhibited a clear bullish breakout above the descending trendline pattern, which has been active since March 2024. In light of that, Sina, the co-founder ...

Bayesian Optimization, widely used in experimental design and black-box optimization, traditionally relies on regression models for predicting the performance of solutions within fixed search spaces.