Results 11 to 20 of about 29,845,221 (313)

Bayesian Regression Analysis for Dependent Data with an Elliptical Shape [PDF]

Entropy
This paper proposes a parametric hierarchical model for functional data with an elliptical shape, using a Gaussian process prior to capturing the data dependencies that reflect systematic errors while modeling the underlying curved shape through a von ...
Yian Yu, Long Tang, Kang Ren, Zhonglue Chen, Shengdi Chen, Jianqing Shi   +5 more
doaj   +2 more sources

Data associated with publication: Polycystin-2 dependent transcriptome reveals potential initiators of autosomal dominant polycystic kidney disease

, 2023
The reposited data contain transcriptome profiles generated from RNA-Seq analysis in a newly developed kidney ADPKD cell line and bioinformatic profiles of cellular processes.
Outeda, Patricia, Welling, Paul A., Woodward, Owen M., Jung, Hyun Jun, Coleman, Richard, Reiter, Jeremy F., Dixon, Eryn E., Watnick, Terry, Cebotaru, Valeriu   +8 more
core   +1 more source

Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks

Journal of Pharmaceutical Analysis, 2022
Lipidomics coverage improvement is essential for functional lipid and pathway construction. A powerful approach to discovering organism lipidome is to combine various data acquisitions, such as full scan mass spectrometry (full MS), data-dependent ...
Nguyen Hoang Anh, Young Cheol Yoon, Young Jin Min, Nguyen Phuoc Long, Cheol Woon Jung, Sun Jo Kim, Suk Won Kim, Eun Goo Lee, Daijie Wang, Xiao Wang, Sung Won Kwon   +10 more
doaj   +1 more source

Work Situation and Stress Perception of Dependent Employees in the Year 2021 Marked by the Corona Pandemic

, 2023
Against the background of COVID-19 and various measures to slow down the spread of the novel coronavirus, the German Federal Ministry for Labor and Social Affairs (BMAS) has commissioned the Institute of Labor Economics (IZA) to document the work ...
Krause-Pilatus, Annabelle, Rinne, Ulf, Bonin, Holger   +2 more
core   +1 more source

Nonparametric analysis of the time structure of seismicity in a geographic region

Annals of Geophysics, 2002
As an alternative to traditional parametric approaches, we suggest nonparametric methods for analyzing temporal data on earthquake occurrences. In particular, the kernel method for estimating the hazard function and the intensity function are presented ...
A. Quintela-del-Río, H. L. Cimadevila, G. Estévez-Pérez   +2 more
doaj   +1 more source

Replication Data Based on the IZA Evaluation Dataset Survey

, 2023
The provided dataset contains survey information on the job search behavior of unemployed job seekers based on the IZA Evaluation Dataset Survey. The survey contains information on respondents who were interviewed 7-14 weeks after they became unemployed
Mahlstedt, Robert, Künn, Steffen, Caliendo, Marco   +2 more
core   +1 more source

Strong consistency of a kernel-based rule for spatially dependent data [PDF]

Arab Journal of Mathematical Sciences, 2020
We consider the kernel-based classifier proposed by Younso (2017). This nonparametric classifier allows for the classification of missing spatially dependent data. The weak consistency of the classifier has been studied by Younso (2017).
Ahmad Younso, Ziad Kanaya, Nour Azhari
doaj   +1 more source

Dependent Functional Data [PDF]

ISRN Probability and Statistics, 2012
This paper reviews recent research on dependent functional data. After providing an introduction to functional data analysis, we focus on two types of dependent functional data structures: time series of curves and spatially distributed curves. We review statistical models, inferential methodology, and possible extensions.
openaire   +1 more source

Gamma Kernel Estimation of the Density Derivative on the Positive Semi-Axis by Dependent Data

Revstat Statistical Journal, 2016
We estimate the derivative of a probability density function defined on [0,∞). For this purpose, we choose the class of kernel estimators with asymmetric gamma kernel functions.
L.A. Markovich
doaj   +1 more source

Quantifying Data Dependencies with Rényi Mutual Information and Minimum Spanning Trees

Entropy, 2019
In this study, we present a novel method for quantifying dependencies in multivariate datasets, based on estimating the Rényi mutual information by minimum spanning trees (MSTs). The extent to which random variables are dependent is an important question,
Anne Eggels, Daan Crommelin
doaj   +1 more source