Bootstrap Analysis of the Lace Expansion

Parametric bootstrapping (BS) provides an attractive alternative, both theoretically and numerically, to asymptotic theory for estimating sampling distributions. This chapter summarizes its use not only for calculating confidence intervals for estimated parameters and functions of parameters, but also to obtain log-likelihood-based confidence regions from which confidence bands for cumulative distribution and regression functions can be obtained. All such BS calculations are very easy to implement. Details are also given for calculating critical values of EDF statistics used in goodness-of-fit (GoF) tests, such as the Anderson-Darling A2 statistic whose null distribution is otherwise difficult to obtain, as it varies with different null hypotheses. A simple proof is given showing that the parametric BS is probabilistically exact for location-scale models. A formal regression lack-of-fit test employing parametric BS is given that can be used even when the regression data has no replications. Two real data examples are given.

Download Full-text

How can organizations foster job crafting behaviors and thriving at work?

Journal of Management & Organization ◽

10.1017/jmo.2020.31 ◽

2020 ◽

pp. 1-18

Author(s):

Sari Mansour ◽

Diane-Gabrielle Tremblay

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Job Demands ◽

Social Resources ◽

Job Crafting ◽

Equation Modeling ◽

Bootstrap Analysis ◽

Mediating Role ◽

Thriving At Work

Abstract This study investigates whether the perceived opportunity to craft (POC) is related to job crafting (JC) strategies and whether these strategies are related to thriving at work, in terms of both vitality and learning. It aims to verify the mediating role of JC between POC and thriving. Data were collected from 424 accounting professionals in Canada. The structural equation modeling based on bootstrap analysis was used to test mediation. The results indicate that POC is positively related to increasing structural and social resources and challenging job demands and negatively to decreasing hindering job demands. They reveal that increasing structural and social resources enhances learning and mediates the relation between POC and vitality and learning, as do challenging job demands, whereas decreasing hindering job demands does not. This study is one of the first to confirm that POC influences vitality and learning via JC behaviors as mediators.

Download Full-text

A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

Applied Sciences ◽

10.3390/app11083636 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3636

Author(s):

Faria Zarin Subah ◽

Kaushik Deb ◽

Pranab Kumar Dhar ◽

Takeshi Koshiba

Keyword(s):

Autism Spectrum Disorder ◽

Resting State ◽

State Of The Art ◽

Resting State Fmri ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Bootstrap Analysis ◽

Proposed Model ◽

Art Methods ◽

The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

Download Full-text

Superconformal boundaries in 4 − ϵ dimensions

Journal of High Energy Physics ◽

10.1007/jhep04(2021)167 ◽

2021 ◽

Vol 2021 (4) ◽

Author(s):

Aleix Gimenez-Grau ◽

Pedro Liendo ◽

Philine van Vliet

Keyword(s):

Three Dimensional ◽

Spacetime Dimension ◽

Perfect Agreement ◽

Bootstrap Analysis ◽

First Order ◽

Free Theory ◽

First Order Correction ◽

Free Parameters ◽

Superconformal Theories ◽

Zumino Model

Abstract Boundaries in three-dimensional $$ \mathcal{N} $$ N = 2 superconformal theories may preserve one half of the original bulk supersymmetry. There are two possibilities which are characterized by the chirality of the leftover supercharges. Depending on the choice, the remaining 2d boundary algebra exhibits $$ \mathcal{N} $$ N = (0, 2) or $$ \mathcal{N} $$ N = (1) supersymmetry. In this work we focus on correlation functions of chiral fields for both types of supersymmetric boundaries. We study a host of correlators using superspace techniques and calculate superconformal blocks for two- and three-point functions. For $$ \mathcal{N} $$ N = (1) supersymmetry, some of our results can be analytically continued in the spacetime dimension while keeping the codimension fixed. This opens the door for a bootstrap analysis of the ϵ-expansion in supersymmetric BCFTs. Armed with our analytically-continued superblocks, we prove that in the free theory limit two-point functions of chiral (and antichiral) fields are unique. The first order correction, which already describes interactions, is universal up to two free parameters. As a check of our analysis, we study the Wess-Zumino model with a super-symmetric boundary using Feynman diagrams, and find perfect agreement between the perturbative and bootstrap results.

Download Full-text

Self-avoiding walk enumeration via the lace expansion

Journal of Physics A Mathematical and Theoretical ◽

10.1088/1751-8113/40/36/003 ◽

2007 ◽

Vol 40 (36) ◽

pp. 10973-11017 ◽

Cited By ~ 48

Author(s):

Nathan Clisby ◽

Richard Liang ◽

Gordon Slade

Keyword(s):

Lace Expansion ◽

Self Avoiding Walk

Download Full-text

Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-7279-2019 ◽

2019 ◽

Vol 19 (11) ◽

pp. 7279-7295 ◽

Cited By ~ 2

Author(s):

Athanasia Vlachou ◽

Anna Tobler ◽

Houssni Lamkaddam ◽

Francesco Canonaco ◽

Kaspar R. Daellenbach ◽

...

Keyword(s):

Matrix Factorization ◽

Organic Aerosol ◽

Positive Matrix Factorization ◽

Water Soluble ◽

Oxidation Products ◽

Positive Matrix ◽

Bootstrap Analysis ◽

Seasonal Behavior ◽

Yearly Cycle ◽

Aerosol Mass

Abstract. Bootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorization (PMF) model. This approach can estimate the factor-related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmentally plausible solutions, though, can be challenging, and a systematic approach to identify and sort the factors is needed. For this, comparison of the factors between each bootstrap run and the initial PMF output, as well as with externally determined markers, is crucial. As a result, certain solutions that exhibit suboptimal factor separation should be discarded. The retained solutions would then be used to test the robustness of the PMF output. Meanwhile, analysis of filter samples with the Aerodyne aerosol mass spectrometer and the application of PMF and bootstrap analysis on the bulk water-soluble organic aerosol mass spectra have provided insight into the source identification and their uncertainties. Here, we investigated a full yearly cycle of the sources of organic aerosol (OA) at three sites in Estonia: Tallinn (urban), Tartu (suburban) and Kohtla-Järve (KJ; industrial). We identified six OA sources and an inorganic dust factor. The primary OA types included biomass burning, dominant in winter in Tartu and accounting for 73 % ± 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % ± 8 % and 11 % ± 5 %, respectively), and two other primary OA types lower in mass. A sulfur-containing OA was related to road dust and tire abrasion which exhibited a rather stable yearly cycle, and an oil OA was connected to the oil shale industries in KJ prevailing at this site that comprises 36 % ± 14 % of the total OA in spring. The secondary OA sources were separated based on their seasonal behavior: a winter oxygenated OA dominated in winter (36 % ± 14 % for KJ, 25 % ± 9 % for Tallinn and 13 % ± 5 % for Tartu) and was correlated with benzoic and phthalic acid, implying an anthropogenic origin. A summer oxygenated OA was the main source of OA in summer at all sites (26 % ± 5 % in KJ, 41 % ± 7 % in Tallinn and 35 % ± 7 % in Tartu) and exhibited high correlations with oxidation products of a-pinene-like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA), suggesting a biogenic origin.

Download Full-text

SAMPLE BIAS RELATED TO HOUSEHOLD ROLE

Journal of Survey Statistics and Methodology ◽

10.1093/jssam/smaa001 ◽

2020 ◽

Author(s):

MARCIN HITCZENKO

Keyword(s):

Sampling Design ◽

Sampling Procedure ◽

Bootstrap Analysis ◽

Electronic Payments ◽

Household Finances ◽

Sample Bias ◽

Household Members ◽

Survey Responses ◽

True Values ◽

Estimation Of Distributions

Abstract This article develops a two-stage statistical analysis to identify and assess the effect of a sample bias associated with an individual’s household role. Survey responses to questions about the respondent’s role in household finances and a sampling design in which some households have all members take the survey enable the estimation of distributions for each individual’s share of household responsibility. The methodology is applied to the 2017 Survey of Consumer Payment Choice. The distribution of responsibility shares among survey respondents suggests that the sampling procedure favors household members with higher levels of responsibility. A bootstrap analysis reveals that population mean estimates of monthly payment instrument use that do not account for this type of sample misrepresentation are likely biased for instruments often used to make household purchases. For checks and electronic payments, our analysis suggests that it is likely that unadjusted estimates overstate true values by 10–20 percent.

Download Full-text