scholarly journals Discussion of ‘Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection’

2020 ◽  
Vol 49 (4) ◽  
pp. 1076-1080
Author(s):  
Haeran Cho ◽  
Claudia Kirch

AbstractWe congratulate the author for this interesting paper which introduces a novel method for the data segmentation problem that works well in a classical change point setting as well as in a frequent jump situation. Most notably, the paper introduces a new model selection step based on finding the ‘steepest drop to low levels’ (SDLL). Since the new model selection requires a complete (or at least relatively deep) solution path ordering the change point candidates according to some measure of importance, a new recursive variant of the Wild Binary Segmentation (Fryzlewicz in Ann Stat 42:2243–2281, 2014, WBS) named WBS2, has been proposed for candidate generation.

2020 ◽  
Vol 49 (4) ◽  
pp. 1099-1105 ◽  
Author(s):  
Piotr Fryzlewicz

AbstractMany existing procedures for detecting multiple change-points in data sequences fail in frequent-change-point scenarios. This article proposes a new change-point detection methodology designed to work well in both infrequent and frequent change-point settings. It is made up of two ingredients: one is “Wild Binary Segmentation 2” (WBS2), a recursive algorithm for producing what we call a ‘complete’ solution path to the change-point detection problem, i.e. a sequence of estimated nested models containing $$0, \ldots , T-1$$ 0 , … , T - 1 change-points, where T is the data length. The other ingredient is a new model selection procedure, referred to as “Steepest Drop to Low Levels” (SDLL). The SDLL criterion acts on the WBS2 solution path, and, unlike many existing model selection procedures for change-point problems, it is not penalty-based, and only uses thresholding as a certain discrete secondary check. The resulting WBS2.SDLL procedure, combining both ingredients, is shown to be consistent, and to significantly outperform the competition in the frequent change-point scenarios tested. WBS2.SDLL is fast, easy to code and does not require the choice of a window or span parameter.


2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1057.2-1057
Author(s):  
Y. Liu ◽  
Y. Huang ◽  
Q. Huang ◽  
S. Sun ◽  
Z. Ji ◽  
...  

Background:Exosomes in synovial fluid (SF) has a close relationship with the pathogenesis of rheumatiod arthritis. As a complex biological fluid, SF presents challenges for exosomes isolation using standard methods, such as ExoquickTM kit and ultracentrifugation.Objectives:The study aims to compared the quality of exosomes separated by ExoquickTM kit (TM), ExoquickTM kit+ExoquickTC kit (TM-TC), ultracentrifugation (UC) and TM-TC+UC(TM-TC-UC) from SF.Methods:Exosomes was separated by TM, TM-TC, UC and TM-TC-UC respectively. The size and concentrations of exosomes were detected by high sensitivity flow cytometry for nanoparticle analysis. Total protein and RNA were extracted from exosomes. SDS-PAGE was used to detect the protein distribution of exosomes. Western blot was used to examine the level of albumin and exosomes marker (TSG101 and CD81).Results:There was no statistic difference in the diameters of exosomes separated by the four methods. The concentrations of exosomes in TM, TM-TC, TM-TC-UC and UC were (5.65±0.93), (3.02±1.19), (1.67±0.25) and (4.61±0.73) *109Particles/mL. The protein concentrations of exosomes separated by the four methods were consistent with the concentrations of exosomes. SDS-PAGE showed that the protein distribution of exosomes separated by the four methods were different. Low levels of albumin were detected in TM-TC and TM-TC-UC, while high levels of albumin in TM and UC. Total RNA concentrations from exosomes in TM-TC was higher than other groups.Conclusion:TM-TC can be used to obtain higher quality exosomes from SF for the study of exosome-enriched components.References:[1]Helwa I, et al, A Comparative Study of Serum Exosome Isolation Using Differential Ultracentrifugation and Three Commercial Reagents. PloS one, 2017. 12(1): p. e0170628-e0170628.Figure 1.A: SDS-PAGE showed the protein distribution of exosomes; B: the detection of albumin, TSG101 and CD81 by western blot.Disclosure of Interests:None declared


2010 ◽  
Vol 19 (17) ◽  
pp. 3603-3619 ◽  
Author(s):  
A. J. SHIRK ◽  
D. O. WALLIN ◽  
S. A. CUSHMAN ◽  
C. G. RICE ◽  
K. I. WARHEIT

2018 ◽  
Vol 141 (5) ◽  
Author(s):  
Yeshaswini Emmi ◽  
Andreas Fiolitakis ◽  
Manfred Aigner ◽  
Franklin Genin ◽  
Khawar Syed

A new model approach is presented in this work for including convective wall heat losses in the direct quadrature method of moments (DQMoM) approach, which is used here to solve the transport equation of the one-point, one-time joint thermochemical probability density function (PDF). This is of particular interest in the context of designing industrial combustors, where wall heat losses play a crucial role. In the present work, the novel method is derived for the first time and validated against experimental data for the thermal entrance region of a pipe. The impact of varying model-specific boundary conditions is analyzed. It is then used to simulate the turbulent reacting flow of a confined methane jet flame. The simulations are carried out using the DLR in-house computational fluid dynamics code THETA. It is found that the DQMoM approach presented here agrees well with the experimental data and ratifies the use of the new convective wall heat losses model.


2021 ◽  
Vol 13 (1) ◽  
pp. 56
Author(s):  
Josephine Njeri Ngure ◽  
Anthony Gichuhi Waititu

A non parametric Auto-Regressive Conditional Heteroscedastic model for financial returns series is considered in which the conditional mean and volatility functions are estimated non-parametrically using Nadaraya Watson kernel. A test statistic for unknown abrupt change point in volatility which takes into consideration conditional heteroskedasticity, dependence, heterogeneity and the fourth moment of financial returns, since kurtosis is a function of the fourth moment is considered. The test is based on L2norm of the conditional variance functions of the squared residuals. A non-parametric change point estimator in volatility of financial returns is further obtained. The consistency of the estimator is shown theoretically and through simulation. An application of the estimator in change point estimation in volatility of United States Dollar/Kenya Shilling exchange rate returns data set is made. Through binary segmentation procedure, three change points in volatility of the exchange rate returns are estimated and further accounted for.


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