Results 91 to 100 of about 4,030,812 (306)

State-of-the-Art in Sequential Change-Point Detection

, 2011
We provide an overview of the state-of-the-art in the area of sequential change-point detection assuming discrete time and known pre- and post-change distributions.
Polunchenko, Aleksey S., Tartakovsky, Alexander G.   +1 more
core   +1 more source

Wild binary segmentation for multiple change-point detection [PDF]

, 2014
We propose a new technique, called wild binary segmentation (WBS), for consistent estimation of the number and locations of multiple change-points in data. We assume that the number of change-points can increase to infinity with the sample size. Due to a
P. Fryzlewicz
semanticscholar   +1 more source

Pruning and Nonparametric Multiple Change Point Detection

, 2017
Change point analysis is a statistical tool to identify homogeneity within time series data. We propose a pruning approach for approximate nonparametric estimation of multiple change points.
James, Nicholas, Matteson, David, Zhang, Wenyu   +2 more
core   +1 more source

Adherence to Protocol Recommendations for Children With Wilms Tumour in Two Consecutive Studies in the United Kingdom and Ireland—Does Variation Matter?

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background and Aims Wilms tumour (WT) has excellent event‐free and overall survival (OS). However, small differences exist between countries participating in the same international study. This led us to examine variation in adherence to protocol recommendations as a potential contributing factor.
Suzanne Tugnait, R. Al‐Saadi, A. Lopez‐Cortes, K. Dzhuma, M. Oostveen, J. Brok, S. Irtan, M. Weeks, J. Bate, S. Baker, R. Williams, M. Powis, D. Saunders, O. Olsen, T. Watson, S. Shelmerdine, S. Vaidya, L. Howell, C. Duncan, G. M. Vujanic, C. Stiller, G. P. Balchin, T. Chowdhury, K. Pritchard‐Jones   +23 more
wiley   +1 more source

Change Point Detection for Diversely Distributed Stochastic Processes Using a Probabilistic Method

Inventions, 2019
Unpredicted deviations in time series data are called change points. These unexpected changes indicate transitions between states. Change point detection is a valuable technique in modeling to estimate unanticipated property changes underlying time ...
Muhammad Rizwan Khan, Biswajit Sarkar
doaj   +1 more source

Evaluating the Utility of Paired Tumor and Germline Targeted DNA Sequencing for Pediatric Oncology Patients: A Single Institution Report

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Objective To evaluate the diagnostic yield and utility of universal paired tumor–normal multigene panel sequencing in newly diagnosed pediatric solid and central nervous system (CNS) tumor patients and to compare the detection of germline pathogenic/likely pathogenic variants (PV/LPVs) against established clinical referral criteria for cancer ...
Natalie Waligorski, Rebecca Ronsley, Shannon M. Stasi, Jeffrey Stevens, Erin R. Rudzinski, Christina M. Lockwood, Sarah E. S. Leary, Bonnie L. Cole, Michelle A. Ting, Vera Paulson   +9 more
wiley   +1 more source

Sequential change-point detection in a multinomial logistic regression model

Open Mathematics, 2020
Change-point detection in categorical time series has recently gained attention as statistical models incorporating change-points are common in practice, especially in the area of biomedicine.
Li Fuxiao, Chen Zhanshou, Xiao Yanting
doaj   +1 more source

Semi-Metric Portfolio Optimization: A New Algorithm Reducing Simultaneous Asset Shocks

Econometrics, 2023
This paper proposes a new method for financial portfolio optimization based on reducing simultaneous asset shocks across a collection of assets.
Nick James, Max Menzies, Jennifer Chan
doaj   +1 more source

Intravitreal GD2‐Specific Chimeric Antigen Receptor T‐Cell Therapy for Refractory Retinoblastoma

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Effective treatments for advanced, treatment‐resistant retinoblastoma (RB) remain limited. GD2‐specific chimeric antigen receptor (CAR) T cells show potent antitumor activity with minimal toxicity but have not previously been evaluated in RB.
Subongkoch Subhadhirasakul, Jatuporn Sujjitjoon, Pa‐thai Yenchitsomanus, Kridtin Jarutatsanangkoon, Ng Wei Loon, Kleebsabai Sanpakit, Jassada Buaboonnam, Chayamon Takpradit, Pornpimon Yuti, Nunghathai Sawasdee, Piriya Luangwattananun, Siripakorn Sangkitporn, Arnan Limmahachai, La‐ongsri Atchaneeyasakul   +13 more
wiley   +1 more source

MEDEP: Maintenance Event Detection for Multivariate Time Series Based on the PELT Approach

Sensors, 2022
Predictive Maintenance (PdM) is one of the most important applications of advanced data science in Industry 4.0, aiming to facilitate manufacturing processes.
Milot Gashi, Heimo Gursch, Hannes Hinterbichler, Stefan Pichler, Stefanie Lindstaedt, Stefan Thalmann   +5 more
doaj   +1 more source