Results 41 to 50 of about 2,350,348 (260)
Statistical Aspects of High-Dimensional Sparse Artificial Neural Network Models
Machine Learning and Knowledge Extraction, 2020 An artificial neural network (ANN) is an automatic way of capturing linear and nonlinear correlations, spatial and other structural dependence among features. This machine performs well in many application areas such as classification and prediction from
Kaixu Yang, Tapabrata Maiti
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A Conversation with Chris Heyde
, 2006 Born in Sydney, Australia, on April 20, 1939, Chris Heyde shifted his interest from sport to mathematics thanks to inspiration from a schoolteacher. After earning an M.Sc. degree from the University of Sydney and a Ph.D.
Glasserman, Paul, Kou, Steven
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Cultural Influences in Probabilistic Thinking [PDF]
, 2012 Concerns about students' difficulties in statistics and probability and a lack of research in this area outside of western countries led to a case study which explored form five (14 to 16 year olds) students' ideas in this area.
Sharma, Sashi
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Parent‐to‐Child Information Disclosure in Pediatric Oncology
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Despite professional consensus regarding the importance of open communication with pediatric cancer patients about their disease, actual practice patterns of disclosure are understudied. Extant literature suggests a significant proportion of children are not told about their diagnosis/prognosis, which is purported to negatively ...
Rachel A. Kentor +12 more
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Predicting SARS-CoV-2 infection among hemodialysis patients using deep neural network methods
Scientific ReportsCOVID-19 has a higher rate of morbidity and mortality among dialysis patients than the general population. Identifying infected patients early with the support of predictive models helps dialysis centers implement concerted procedures (e.g., temperature ...
Lihao Xiao +6 more
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An Analysis of Implied Volatility, Sensitivity, and Calibration of the Kennedy Model
MathematicsThe Kennedy model provides a flexible and mathematically consistent framework for modeling the term structure of interest rates, leveraging Gaussian random fields to capture the dynamics of forward rates.
Dalma Tóth-Lakits +2 more
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On Drift Parameter Estimation in Models with Fractional Brownian Motion by Discrete Observations
Austrian Journal of Statistics, 2014 We study a problem of an unknown drift parameter estimation in a stochastic differen- tial equation driven by fractional Brownian motion. We represent the likelihood ratio as a function of the observable process.
Yuliya Mishura, Kostiantyn Ralchenko
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Investigation of sample paths properties for some classes of φ-sub-Gaussian stochastic processes
Modern Stochastics: Theory and Applications, 2021 This paper investigates sample paths properties of φ-sub-Gaussian processes by means of entropy methods. Basing on a particular entropy integral, we treat the questions on continuity and the rate of growth of sample paths.
Olha Hopkalo, Lyudmyla Sakhno
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Evaluating probability forecasts
, 2012 Probability forecasts of events are routinely used in climate predictions, in forecasting default probabilities on bank loans or in estimating the probability of a patient's positive response to treatment.
Gross, Shulamith T. +2 more
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Molecular Oncology, EarlyView.This study explores salivary RNA for breast cancer (BC) diagnosis, prognosis, and follow‐up. High‐throughput RNA sequencing identified distinct salivary RNA signatures, including novel transcripts, that differentiate BC from healthy controls, characterize histological and molecular subtypes, and indicate lymph node involvement.
Nicholas Rajan +9 more
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