Machine learning-based forecasting of CO<sub>2</sub>-related economic growth and agricultural land change in IORA countries. [PDF]
Xu X, Liang G, Rogers RA, Siow CZ.
europepmc +1 more source
Nonparametric maximum likelihood estimation of the survival function using current lifetime data
Abstract An issue when estimating the failure time survival function is how to set up a prevalent cohort study infrastructure to follow subjects after enrollment. This problem can be circumvented through the well‐known Grenander density estimator using current lifetime observations only.
James H. McVittie, Masoud Asgharian
wiley +1 more source
Low-latency stage-adaptive cascade architecture for real time non-stationary noise filtering. [PDF]
Han-Trong T +3 more
europepmc +1 more source
Optimal subsampling for regression with mixed‐type predictors
Abstract Subsampling has emerged as an appealing strategy to mitigate the computational and storage challenges imposed by large datasets. Recent subsampling techniques have shown notable computational gains for data dominated by numerical predictors. However, real‐world datasets frequently contain both numerical and categorical predictors.
Jiaqing Zhu, Lin Wang, Fasheng Sun
wiley +1 more source
A predictive model for flow index performance of pit drip irrigation emitters using BP-PSO algorithm. [PDF]
Xu W, Jiang J, Xu T.
europepmc +1 more source
Homophily‐adjusted social influence estimation
Abstract Homophily and social influence are two key concepts of social network analysis. Distinguishing between these phenomena is difficult, and approaches to disambiguate the two have been primarily limited to longitudinal data analyses. In this study, we provide sufficient conditions for valid estimation of social influence through cross‐sectional ...
Hanh T.D. Pham, Daniel K. Sewell
wiley +1 more source
Channel estimation strategies for STAR-RIS-Aided NOMA: robustness to imperfections and complexity trade-offs for 6G wireless systems. [PDF]
Ammisetty MB, Ramarakula M.
europepmc +1 more source
Extreme conditional quantile estimation for time series
Abstract We consider the estimation of an extreme conditional quantile QY(1−p|x0)$$ {Q}_Y\left(1-p|{x}_0\right) $$ for a heavy‐tailed distribution in the case of a strictly stationary time series (Xt,Yt)t∈ℤ$$ {\left({X}_t,{Y}_t\right)}_{t\in \mathbb{Z}} $$. Here, QY(·|x0)$$ {Q}_Y\left(\cdotp |{x}_0\right) $$ denotes the conditional quantile function of
Yuri Goegebeur +2 more
wiley +1 more source
Research on measurement technology of optical fiber angle sensor based on MEA-BP. [PDF]
Lisha W +6 more
europepmc +1 more source
Development and Test of Highly Accurate End Point Free Energy Methods. 4. Expanding Solvents Capability and logBB Prediction. [PDF]
Niu T, He X, Man VH, Wang X, Wang J.
europepmc +1 more source

