The bootstrap and Bayesian bootstrap method in assessing bioequivalence

2009 ◽  
Vol 41 (5) ◽  
pp. 2246-2249 ◽  
Author(s):  
Wan Jianping ◽  
Zhang Kongsheng ◽  
Chen Hui
Keyword(s):  
Bootstrap Method ◽  
Bayesian Bootstrap

scholarly journals Election polling is not dead: A Bayesian bootstrap method yields accurate forecasts

2021 ◽  
Author(s):  
Henrik Olsson
Keyword(s):  
Social Science ◽  
Bootstrap Method ◽  
New Method ◽  
Social Contacts ◽  
Independent Evidence ◽  
Bayesian Bootstrap ◽  
Bayesian Forecast ◽  
Private And Public ◽  
Public Evidence

We present a new Bayesian bootstrap method for election forecasts that combines traditional polling questions about people’s own intentions with their expectations about how others will vote. It treats each participant’s election winner expectation as an optimal Bayesian forecast given private and public evidence available to that individual. It then infers the independent evidence and aggregates it across participants. The bootstrap forecast outperforms aggregate national polls in the 2020 U.S. election, as well as the forecasts based on traditional polling questions posed on large national probabilistic samples before the 2018 and 2020 U.S. elections. The bootstrap forecast puts most weight on people’s expectations about how their social contacts will vote, which might incorporate information about voters who are difficult to reach or who hide their true intentions. Beyond election polling, the new method is expected to improve the validity of other social science surveys.

scholarly journals A Bootstrap Method to Test Granger-Causality in the Frequency Domain

2021 ◽  
Author(s):  
Matteo Farnè ◽  
Angela Montanari
Keyword(s):  
Stochastic Process ◽  
Frequency Domain ◽  
Granger Causality ◽  
Bootstrap Method ◽  
Money Stock ◽  
Bootstrap Test ◽  
Low Frequencies ◽  
Economic Output ◽  
Stationary Bootstrap ◽  
The Relationship

AbstractWe propose a bootstrap test for unconditional and conditional Granger-causality spectra in the frequency domain. Our test aims to detect if the causality at a particular frequency is systematically different from zero. In particular, we consider a stochastic process derived applying independently the stationary bootstrap to the original series. At each frequency, we test the sample causality against the distribution of the median causality across frequencies estimated for that process. Via our procedure, we infer about the relationship between money stock and GDP in the Euro Area during the period 1999–2017. We point out that the money stock aggregate M1 had a significant impact on economic output at all frequencies, while the opposite relationship is significant only at low frequencies.

scholarly journals Evaluation of Inter-Observer Reliability of Animal Welfare Indicators: Which Is the Best Index to Use?

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1445
Author(s):  
Mauro Giammarino ◽  
Silvana Mattiello ◽  
Monica Battini ◽  
Piero Quatto ◽  
Luca Maria Battaglini ◽  
...  
Keyword(s):  
Animal Welfare ◽  
Confidence Intervals ◽  
Bootstrap Method ◽  
Dairy Goat ◽  
Microsoft Excel ◽  
Bootstrap Methods ◽  
Welfare Indicators ◽  
Observer Reliability ◽  
High Concordance ◽  
Variance Estimates

This study focuses on the problem of assessing inter-observer reliability (IOR) in the case of dichotomous categorical animal-based welfare indicators and the presence of two observers. Based on observations obtained from Animal Welfare Indicators (AWIN) project surveys conducted on nine dairy goat farms, and using udder asymmetry as an indicator, we compared the performance of the most popular agreement indexes available in the literature: Scott’s π, Cohen’s k, kPABAK, Holsti’s H, Krippendorff’s α, Hubert’s Γ, Janson and Vegelius’ J, Bangdiwala’s B, Andrés and Marzo’s ∆, and Gwet’s γ(AC1). Confidence intervals were calculated using closed formulas of variance estimates for π, k, kPABAK, H, α, Γ, J, ∆, and γ(AC1), while the bootstrap and exact bootstrap methods were used for all the indexes. All the indexes and closed formulas of variance estimates were calculated using Microsoft Excel. The bootstrap method was performed with R software, while the exact bootstrap method was performed with SAS software. k, π, and α exhibited a paradoxical behavior, showing unacceptably low values even in the presence of very high concordance rates. B and γ(AC1) showed values very close to the concordance rate, independently of its value. Both bootstrap and exact bootstrap methods turned out to be simpler compared to the implementation of closed variance formulas and provided effective confidence intervals for all the considered indexes. The best approach for measuring IOR in these cases is the use of B or γ(AC1), with bootstrap or exact bootstrap methods for confidence interval calculation.

scholarly journals Accelerating Green Innovation Performance from the Relations of Network Potential, Absorptive Capacity, and Environmental Turbulence

2021 ◽  
Vol 13 (14) ◽  
pp. 7765
Author(s):  
Shuizheng Song ◽  
Md Altab Hossin ◽  
Xiaohua Yin ◽  
Md Sajjad Hosain
Keyword(s):  
Sustainable Development ◽  
Absorptive Capacity ◽  
Bootstrap Method ◽  
Manufacturing Firms ◽  
Innovation Performance ◽  
Mediation Effect ◽  
Environmental Turbulence ◽  
High Tech ◽  
Green Innovation ◽  
The Relationship

The demand for sustainable development and the advantages of industries are expediting over time with the triggering of green innovation performance (GIP). Improving a firm’s GIP, especially in manufacturing industries, can accelerate green development and mitigate the global-concerned environmental issues. Thus, to investigate GIP from its antecedent factors, we delineate the relationship between network potential, absorptive capacity, environmental turbulence, and GIP based on social network theory, organizational learning theory, and contingency theory. We tested our hypotheses based on 233 sets of questionnaire surveys from high-tech manufacturing firms in China through deploying the hierarchical regression and bootstrap method. Our empirical findings reveal that the network potential dimensions, including network position centrality (NPC), network structure richness (NSR), and network relationship closeness (NRC), significantly positively impacted the GIP. The absorptive capacity (AC) partially mediated the relationship between the network potential dimensions and GIP. Environmental turbulence (ET) as an essential mechanism not only positively moderated the relationship between AC and GIP but also enhanced the AC mediation effect. These findings indicate that manufacturing firms should continue to improve network potential and AC and respond rapidly to changes in the external environment to enhance GIP, consequently contributing to the sustainable development of the economy.

scholarly journals Consistency of bootstrap approximation to the null distributions of local spatial statistics with application to house price analysis

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Chang-Lin Mei ◽  
Shou-Fang Xu ◽  
Feng Chen
Keyword(s):  
Spatial Statistics ◽  
Bootstrap Method ◽  
Spatial Association ◽  
House Price ◽  
Housing Price ◽  
Bootstrap Approximation ◽  
Local Statistics ◽  
Null Distributions ◽  
Local Spatial Statistics ◽  
Distribution Of A Statistic

Abstract With the increasing availability of spatially extensive geo-referenced data, much attention has been paid to the use of local statistics to identify local patterns of spatial association, in which the null distributions of local statistics play an essential role in the related statistical inference. As a powerful tool to approximate the distribution of a statistic, the bootstrap method is used in this paper to derive null distributions of the commonly used local spatial statistics including local Getis and Ord’s $G_{i}$ G i , Moran’s $I_{i}$ I i and Geary’s $c_{i}$ c i . Strong consistency of the bootstrap approximation to the null distributions of the statistics is proved under some mild conditions, and the Boston housing price data are analyzed to demonstrate the application of the theoretical results.

scholarly journals Statistical Estimates of the Pulsar Glitch Activity

Universe ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 8
Author(s):  
Alessandro Montoli ◽  
Marco Antonelli ◽  
Brynmor Haskell ◽  
Pierre Pizzochero
Keyword(s):  
Linear Regression ◽  
Upper Bound ◽  
Bootstrap Method ◽  
Waiting Times ◽  
Statistical Estimates ◽  
Equal Variance ◽  
Linear Fit ◽  
The Moment ◽  
The Bootstrap Method

A common way to calculate the glitch activity of a pulsar is an ordinary linear regression of the observed cumulative glitch history. This method however is likely to underestimate the errors on the activity, as it implicitly assumes a (long-term) linear dependence between glitch sizes and waiting times, as well as equal variance, i.e., homoscedasticity, in the fit residuals, both assumptions that are not well justified from pulsar data. In this paper, we review the extrapolation of the glitch activity parameter and explore two alternatives: the relaxation of the homoscedasticity hypothesis in the linear fit and the use of the bootstrap technique. We find a larger uncertainty in the activity with respect to that obtained by ordinary linear regression, especially for those objects in which it can be significantly affected by a single glitch. We discuss how this affects the theoretical upper bound on the moment of inertia associated with the region of a neutron star containing the superfluid reservoir of angular momentum released in a stationary sequence of glitches. We find that this upper bound is less tight if one considers the uncertainty on the activity estimated with the bootstrap method and allows for models in which the superfluid reservoir is entirely in the crust.

scholarly journals Reliability estimation for drive axle of wheel loader under extreme small sample

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983684 ◽  
Author(s):  
Leilei Cao ◽  
Lulu Cao ◽  
Lei Guo ◽  
Kui Liu ◽  
Xin Ding
Keyword(s):  
Sample Size ◽  
Promising Result ◽  
Small Sample Size ◽  
Bootstrap Method ◽  
Estimation Method ◽  
Small Sample ◽  
Reliability Estimation ◽  
Fatigue Reliability ◽  
Drive Axle ◽  
Semi Empirical

It is difficult to have enough samples to implement the full-scale life test on the loader drive axle due to high cost. But the extreme small sample size can hardly meet the statistical requirements of the traditional reliability analysis methods. In this work, the method of combining virtual sample expanding with Bootstrap is proposed to evaluate the fatigue reliability of the loader drive axle with extreme small sample. First, the sample size is expanded by virtual augmentation method to meet the requirement of Bootstrap method. Then, a modified Bootstrap method is used to evaluate the fatigue reliability of the expanded sample. Finally, the feasibility and reliability of the method are verified by comparing the results with the semi-empirical estimation method. Moreover, from the practical perspective, the promising result from this study indicates that the proposed method is more efficient than the semi-empirical method. The proposed method provides a new way for the reliability evaluation of costly and complex structures.

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