Results 1 to 10 of about 7,646,271 (290)
Point Processes in a Metric Space and Their Applications
Point processes are important in extreme value theory due to their equivalent formulations of two popular models in various applications: the block maxima models and the peak-over-threshold model.
Yuwei Zhao
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Stute presented the so-called conditional U-statistics generalizing the Nadaraya–Watson estimates of the regression function. Stute demonstrated their pointwise consistency and the asymptotic normality.
Salim Bouzebda, Inass Soukarieh
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Signature of Alzheimer’s Disease in Intestinal Microbiome: Results From the AlzBiom Study
BackgroundChanges in intestinal microbiome composition have been described in animal models of Alzheimer’s disease (AD) and AD patients. Here we investigated how well taxonomic and functional intestinal microbiome data and their combination with clinical
Christoph Laske +13 more
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Clonal tracing reveals diverse patterns of response to immune checkpoint blockade
Background Immune checkpoint blockade (ICB) therapy has improved patient survival in a variety of cancers, but only a minority of cancer patients respond.
Shengqing Stan Gu +25 more
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The convergence rate for free-distribution functional data analyses is challenging. It requires some advanced pure mathematics functional analysis tools.
Ouahiba Litimein +4 more
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Probabilistic Wind Speed Forecasting for Wind Turbine Allocation in the Power Grid
To face the growing electricity demand, several countries have adopted the solution of clean energy and use renewable energy sources (e.g., wind and solar) to reinforce the stability of the power network, especially during peak demand periods ...
Mohamed Chaouch
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A Note on Nonparametric Estimation of Conditional Hazard Quantile Function [PDF]
In this paper, we study an kernel estimator of the conditional hazard quantile function (CHQF) of a scalar response variable Y given a random variable (rv) X taking values in a semi-metric space and using the proposed estimator based of the kernel ...
El Hadj Hamel, Nadia Kadiri, Abbes Rabhi
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A Novel Curve Clustering Method for Functional Data: Applications to COVID-19 and Financial Data
Functional data analysis has significantly enriched the landscape of existing data analysis methodologies, providing a new framework for comprehending data structures and extracting valuable insights. This paper is dedicated to addressing functional data
Ting Wei, Bo Wang
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Weakly dependent functional data [PDF]
Functional data often arise from measurements on fine time grids and are obtained by separating an almost continuous time record into natural consecutive intervals, for example, days.
Hörmann, Siegfried, Kokoszka, Piotr
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Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R
We present growfunctions for R that offers Bayesian nonparametric estimation models for analysis of dependent, noisy time series data indexed by a collection of domains.
Terrance D. Savitsky
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