Results 101 to 110 of about 536,403 (288)

Computational limits to nonparametric estimation for ergodic processes

, 2011
A new negative result for nonparametric estimation of binary ergodic processes is shown. I The problem of estimation of distribution with any degree of accuracy is studied.
Takahashi, Hayato
core   +1 more source

Perspectives in educating molecular pathologists on liquid biopsy: Toward integrative, equitable, and decentralized precision oncology

Molecular Oncology, EarlyView.
Liquid biopsy enables minimally invasive, real‐time molecular profiling through analysis of circulating biomarkers in biological fluids. This Perspective highlights the importance of training pathologists through integrative educational programs, such as the European Masters in Molecular Pathology, to ensure effective and equitable implementation of ...
Marius Ilié, Umberto Malapelle, Catherine Alix‐Panabières, Claus Lindbjerg Andersen, Sandra Chlebowski, Vivien Lake, Caroline Lacoux, Virginie Lespinet‐Fabre, Olivier Bordone, Simon Heeke, Christophe Bontoux, Ellen Heitzer, Klaus Pantel, Paul Hofman   +13 more
wiley   +1 more source

The Markov-switching multi-fractal model of asset returns: GMM estimation and linear forecasting of volatility [PDF]

Multi-fractal processes have recently been proposed as a new formalism for modelling the time series of returns in finance. The major attraction of these processes is their ability to generate various degrees of long memory in different powers of returns
Lux, Thomas
core  

Transcriptional network analysis of PTEN‐protein‐deficient prostate tumors reveals robust stromal reprogramming and signs of senescent paracrine communication

Molecular Oncology, EarlyView.
Combining PTEN protein assessment and transcriptomic profiling of prostate tumors, we uncovered a network enriched in senescence and extracellular matrix (ECM) programs associated with PTEN loss and conserved in a mouse model. We show that PTEN‐deficient cells trigger paracrine remodeling of the surrounding stroma and this information could help ...
Ivana Rondon‐Lorefice, Jose I. Lopez, Aitziber Ugalde‐Olano, Maite Zufiaurre, Ianire Astobiza, Natalia Martin‐Martin, Laura Bozal‐Basterra, Saioa Garcia‐Longarte, Amaia Zabala‐Letona, Sofia Rey, Aida Santos‐Martin, Miguel Unda, Ana Loizaga‐Iriarte, Mariona Graupera, Paolo Nuciforo, Arkaitz Carracedo, Isabel Mendizabal   +16 more
wiley   +1 more source

Very short-term probabilistic and scenario-based forecasting of solar irradiance using Markov-chain mixture distribution modeling

Solar Energy Advances
This study investigates probabilistic and scenario-based forecasting of solar irradiance with Markov-chain mixture (MCM) distribution modeling, Persistence Ensemble (PeEn) and Climatology.
Joakim Munkhammar
doaj   +1 more source

Distribution System Load and Forecast Model

, 2014
This short document provides experimental evidence for the set of assumptions on the mean load and forecast errors made in \cite{Sevlian2014A_Outage} and \cite{Sevlian2014B_Outage}. We show that the mean load at any given node is distributed normally, where we compute the mean and variance.
Sevlian, Raffi, Patel, Siddarth, Rajagopal, Ram   +2 more
openaire   +2 more sources

Forecasting the density of asset returns [PDF]

In this paper we introduce a transformation of the Edgeworth-Sargan series expansion of the Gaussian distribution, that we call Positive Edgeworth-Sargan (PES).
Javier Perote, Trino-Manuel Niguez
core  

Next‐generation proteomics improves lung cancer risk prediction

Molecular Oncology, EarlyView.
This is one of very few studies that used prediagnostic blood samples from participants of two large population‐based cohorts. We identified, evaluated, and validated an innovative protein marker model that outperformed an established risk prediction model and criteria employed by low‐dose computed tomography in lung cancer screening trials.
Megha Bhardwaj, Clara Frick, Ben Schöttker, Bernd Holleczek, Hermann Brenner   +4 more
wiley   +1 more source

Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling

Energies
The accurate probabilistic forecasting of ultra-short-term power generation from distributed photovoltaic (DPV) systems is of great significance for optimizing electricity markets and managing energy on the user side.
Yubo Wang, Chao Huo, Fei Xu, Libin Zheng, Ling Hao   +4 more
doaj   +1 more source

Distributional Refinement Network: Distributional Forecasting via Deep Learning

A key task in actuarial modelling involves modelling the distributional properties of losses. Classic (distributional) regression approaches like Generalized Linear Models (GLMs; Nelder and Wedderburn, 1972) are commonly used, but challenges remain in developing models that can (i) allow covariates to flexibly impact different aspects of the ...
Avanzi, Benjamin, Dong, Eric, Laub, Patrick J., Wong, Bernard   +3 more
openaire   +2 more sources