Percentile Bootstrap Confidence Interval Research Articles

The Effect of a Repeated Sprint Training Session on Neuromuscular Acute Fatigue

The performance in countermovement jump (CMJ) may represent a practical and noninvasive way to control the effects imposed by loads of a training session on fatigue. The present study aimed to investigate the acute influence of repeated sprint training (RST) protocol on neuromuscular performance, measured with countermovement jump (CMJ) and a 40m sprint test, at different moments - during and post training; and to check if the sprint decrement obtained throughout the RST protocol would be related to the acute decrement in CMJ. The sample was composed of 11 male university-level team-sport athletes (22.66±1.97 years, 75.68±7.84 kg, 1.78±2.06 m). After the familiarization process, the volunteers participated in the CMJ baseline test, RST training session, CMJ test during the RST training, CMJ test after RST training (2h, 24h and 48h). The minimal individual difference (MID) was used to identify actual individual changes in CMJ and sprint performance. Pearson’s correlation coefficient, with 95% percentile bootstrap confidence intervals, was used to identify the relationship between CMJ and sprint performance. The RST training protocol was able to induce real CMJ and sprint performance changes. The CMJ performance returned to initial values 24 and 48 hours after RST training. The sprint performance remained altered during the RST, which was expected. However, performance remained below MID at 2h, 24h and 48h after the training protocol. The performance at CMJ and sprint showed a positive and significant correlation. A multi-parameter approach should be adopted to monitor the status of neuromuscular fatigue.

Correcting the Bias Correction for the Bootstrap Confidence Interval in Mediation Analysis.

The bias-corrected bootstrap confidence interval (BCBCI) was once the method of choice for conducting inference on the indirect effect in mediation analysis due to its high power in small samples, but now it is criticized by methodologists for its inflated type I error rates. In its place, the percentile bootstrap confidence interval (PBCI), which does not adjust for bias, is currently the recommended inferential method for indirect effects. This study proposes two alternative bias-corrected bootstrap methods for creating confidence intervals around the indirect effect: one originally used by Stine (1989) with the correlation coefficient, and a novel method that implements a reduced version of the BCBCI's bias correction. Using a Monte Carlo simulation, these methods were compared to the BCBCI, PBCI, and Chen and Fritz (2021)'s 30% Winsorized BCBCI. The results showed that the methods perform on a continuum, where the BCBCI has the best balance (i.e., having closest to an equal proportion of CIs falling above and below the true effect), highest power, and highest type I error rate; the PBCI has the worst balance, lowest power, and lowest type I error rate; and the alternative bias-corrected methods fall between these two methods on all three performance criteria. An extension of the original simulation that compared the bias-corrected methods to the PBCI after controlling for type I error rate inflation suggests that the increased power of these methods might only be due to their higher type I error rates. Thus, if control over the type I error rate is desired, the PBCI is still the recommended method for use with the indirect effect. Future research should examine the performance of these methods in the presence of missing data, confounding variables, and other real-world complications to enhance the generalizability of these results.

Age-stage, two-sex life table and predation parameters of Harmonia axyridis Pallas (Coleoptera: Coccinellidae), reared on Acyrthosiphon pisum (Harris) (Hemiptera: Aphididae), at four different temperatures

Pest management strategies relying on biocontrol agents may be altered by climate change because temperature regulates biological rates from individuals to populations. We investigated predation and development of Harmonia axyridis foraging on Acyrthosiphon pisum at four different temperatures (15, 20, 25, and 30 °C). Age-stage, two-sex life table analysis of the predator was conducted. Computer projections based on bootstrap percentile confidence intervals were used for assessment of population growth and predation parameters. H. axyridis development and predation varied across temperature. Development was much slower at 15 °C than at warmer temperatures. Pre-adult stage mean duration was 41.25 d at 15 °C, but this decreased to 28.67, 18.35, and 13.23 d at 20, 25, and 30 °C, respectively. The intrinsic rate of increase was 0.0805 d−1 at 15 °C, but this increased to 0.1009, 0.1324, 0.1813 d−1 at 20, 25, and 30 °C, respectively. The mean generation time (T) was 71.96 d at 15 °C, which decreased to 54.68, 42.64 and 29.96 d at 20, 25, and 30 °C, respectively. The highest intrinsic rate of increase (r) and finite rate of increase (λ) were obtained at 30 °C, while the highest fecundity occurred at 15 °C with 743.68 eggs/female. The net predation rates (C0) were 4445.28, 4299.30, 3602.18, and 2624.20 aphids at 15, 20, 25, and 30 °C, respectively. Population and predation projections were proportional to temperature. These results are useful for modelling the population response of H. axyridis to climate change and tailoring IPM strategies to altered climates.

Summary Intervals for Model-Based Classification Accuracy and Consistency Indices.

When scores are used to make decisions about respondents, it is of interest to estimate classification accuracy (CA), the probability of making a correct decision, and classification consistency (CC), the probability of making the same decision across two parallel administrations of the measure. Model-based estimates of CA and CC computed from the linear factor model have been recently proposed, but parameter uncertainty of the CA and CC indices has not been investigated. This article demonstrates how to estimate percentile bootstrap confidence intervals and Bayesian credible intervals for CA and CC indices, which have the added benefit of incorporating the sampling variability of the parameters of the linear factor model to summary intervals. Results from a small simulation study suggest that percentile bootstrap confidence intervals have appropriate confidence interval coverage, although displaying a small negative bias. However, Bayesian credible intervals with diffused priors have poor interval coverage, but their coverage improves once empirical, weakly informative priors are used. The procedures are illustrated by estimating CA and CC indices from a measure used to identify individuals low on mindfulness for a hypothetical intervention, and R code is provided to facilitate the implementation of the procedures.

3D CAIPIRINHA SPACE versus standard 2D TSE for routine knee MRI: a large-scale interchangeability study.

To perform a large-scale interchangeability study comparing 3D controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) sampling perfection with application optimized contrast using different flip angle evolutions (SPACE) TSE with standard 2D TSE for knee MRI. In this prospective study, 250 patients underwent 3 T knee MRI, including a multicontrast 3D CAIPIRINHA SPACE TSE (9:26 min) and a standard 2D TSE protocol (12:14 min). Thirty-three (13%) patients had previous anterior cruciate ligament and/or meniscus surgery. Two radiologists assessed MRIs for image quality and identified pathologies of menisci, ligaments, and cartilage by using a 4-point Likert scale according to the level of diagnostic confidence. Interchangeability of the protocols was tested under the same-reader scenario using a bootstrap percentile confidence interval. Interreader reliability and intermethod concordance were also evaluated. Despite higher image quality and diagnostic confidence for standard 2D TSE compared to 3D CAIPIRINHA SPACE TSE, the protocols were found interchangeable for diagnosing knee abnormalities, except for patellar (6.8% difference; 95% CI: 4.0, 9.6) and trochlear (3.6% difference; 95% CI: 0.8, 6.6) cartilage defects. The interreader reliability was substantial to almost perfect for 2D and 3D MRI (range κ, 0.785-1 and κ, 0.725-0.964, respectively). Intermethod concordance was almost perfect for all diagnoses (range κ, 0.817-0.986). Multicontrast 3D CAIPIRINHA SPACE TSE and standard 2D TSE protocols perform interchangeably for diagnosing knee abnormalities, except for patellofemoral cartilage defects. Despite the radiologist's preference for 2D TSE imaging, a pursuit towards time-saving 3D TSE knee MRI is justified for routine practice. • Multicontrast 3D CAIPIRINHA SPACE and standard 2D TSE protocols perform interchangeably for diagnosing knee abnormalities, except for patellofemoral cartilage defects. • Radiologists are more confident in diagnosing knee abnormalities on 2D TSE than on 3D CAIPIRINHA SPACE TSE MRI. • Despite the radiologist's preference for 2D TSE, a pursuit towards accelerated 3D TSE knee MRI is justified for routine practice.

Measurement of dispersion of PM 2.5 in Thailand using confidence intervals for the coefficient of variation of an inverse Gaussian distribution.

Air pollution is a growing concern for the general public in Thailand with PM 2.5 (particulate matter ≤ 2.5 µm) having the greatest impact on health. The inverse Gaussian (IG) distribution is used for examining the frequency of high concentration events and has often been applied to analyze pollution data, with the coefficient of variation (CV) being used to calculate the quantitative difference in PM 2.5 concentrations. Herein, we propose confidence intervals for the CV of an IG distribution based on the generalized confidence interval (GCI), the adjusted generalized confidence interval (AGCI), the bootstrap percentile confidence interval (BPCI), the fiducial confidence interval (FCI), and the fiducial highest posterior density confidence interval (F-HPDCI). The performance of the proposed confidence intervals was evaluated by using their coverage probabilities and average lengths from various scenarios via Monte Carlo simulations. The simulation results indicate that the coverage probabilities of the AGCI and FCI methods were higher than or close to the nominal level in all of test case scenarios. Moreover, FCI outperformed the others for small sample sizes by achieving the shortest average length. The efficacies of the confidence intervals were demonstrated by using PM 2.5 data from the Din Daeng and Bang Khun Thian districts in Bangkok, Thailand.

Competing Risks Step-Stress Model with Lagged Effect under Gompertz Distribution

In many survival analysis studies, the failure of a product may be attributed to one of several competing risks. In addition, if survival time is long, researchers can adopt accelerated life tests, causing devices to fail more quickly. One popular type of accelerated life tests is the step-stress test, and in this test, the stress level changes at a predetermined point time. The manner that stress levels change abruptly and increase discontinuously has been studied extensively. This paper considers a more realistic situation where the effect of stress increases cannot be achieved all at once, but with a lag time, and we propose a step-stress model consisting of two independent competing risks with a lag period in which the failure time caused by different risks at different stress levels obey Gompertz distribution, and the range of lag period is predetermined. The unknown parameters are estimated by maximum likelihood estimation and least squares estimation. For comparison, asymptotic confidence intervals and percentile bootstrap confidence intervals are constructed. By using Monte-Carlo simulations, we obtain the means and mean square errors of the maximum likelihood estimates and the least squares estimates, as well as the mean lengths and coverage rates of the two confidence intervals, which show the performance of various methods. Finally, in order to illustrate the model and proposed methods, we analyze a dataset from a solar energy experiment.

A note on the coverage behaviour of bootstrap percentile confidence intervals for constrained parameters

The asymptotic behaviour of the commonly used bootstrap percentile confidence interval is investigated when the parameters are subject to linear inequality constraints. We concentrate on the important one- and two-sample problems with data generated from general parametric distributions in the natural exponential family. The focus of this paper is on quantifying the coverage probabilities of the parametric bootstrap percentile confidence intervals, in particular their limiting behaviour near boundaries. We propose a local asymptotic framework to study this subtle coverage behaviour. Under this framework, we discover that when the true parameters are on, or close to, the restriction boundary, the asymptotic coverage probabilities can always exceed the nominal level in the one-sample case; however, they can be, remarkably, both under and over the nominal level in the two-sample case. Using illustrative examples, we show that the results provide theoretical justification and guidance on applying the bootstrap percentile method to constrained inference problems.

The Gamma–Gompertz distribution: Theory and applications

In this article, we explore a new model with three parameters called Gamma–Gompertz (GGo) distribution which is generated by the gamma-X family. The GGo distribution provides better fits than some competing distributions such as three-parameter Gompertz, Gompertz–Lindley, Gompertz–geometric, Gompertz–Poisson, and inverse Gompertz distributions. Some well-known distributions are included in the GGo distribution as special sub-models. The density function of the distribution can be decreasing, unimodal and decreasing–increasing–decreasing shaped while the failure rate function can be increasing and unimodal shaped. Various properties of the GGo distribution are obtained. An extensive simulation study is carried out to assess the effectiveness of some classical estimation approaches which are discussed to estimate the model parameters. To demonstrate the potentiality of the GGo distribution, two real data sets are used and the bootstrap percentile confidence intervals are also obtained by bootstrap resampling. In addition, a new regression model is proposed via re-parametrization of the GGo distribution, called the log-GGo distribution. The maximum likelihood method is considered to estimate the unknown parameters of re-parametrized log-GGo distribution. The potentiality of log-GGo regression model is analyzed for HIV+ censored data set.

Are Japanese Women Less Physically Active Than Men? Findings From the DOSANCO Health Study.

BackgroundPrevious research has established that women accumulate less moderate-to-vigorous physical activity (MVPA) than men. To date, however, little is known about the gender differences in device-based activity patterns of sedentary behavior (SB) and light-intensity physical activity (LPA). We aimed to compare time spent in SB and different intensities of physical activity taking into account of co-dependence of time use domains.MethodsThis cross-sectional study was conducted in Suttu town, Hokkaido, Japan. Data were analyzed from 634 Japanese adults (278 men, aged 19–92 years) who provided valid accelerometer (HJA-750C) data. Gender differences in activity behavior patterns were tested using multivariate analysis of covariance (MANCOVA) based on isometric log-ratio transformations of time use, adjusting for age. We also developed bootstrap percentile confidence intervals (CI) to support the interpretation of which behavior differed between genders.ResultsOverall, participants had percent time spent in SB, LPA, MVPA during wearing time (mean, 14.8 hours) corresponding to 53.9%, 41.7%, and 4.4% of wearing time, respectively. Activity behavior patterns differed significantly between genders after controlling for time spent in all activities. Women spent relatively 13.3% (95% CI, 9.9–15.9%) less time in SB and 19.8% (95% CI, 14.9–24.6%) more time in LPA compared to men. The difference of time spent in MVPA was not statistically significant.ConclusionsIn contrast with previous studies, our findings suggest that Japanese women are more physically active than men when all intensities of activities are considered. Given the health benefits of LPA, evaluating only MVPA may disproportionately underestimate the level of physical activity of women.

Bivariate Weibull-G Family Based on Copula Function: Properties, Bayesian and non-Bayesian Estimation and Applications

This paper aims to obtain a new flexible bivariate generalized family of distributions based on FGM copula, which is called bivariate FGM Weibull-G family. Some of its statistical properties are studied as marginal distributions, product moments, and moment generating functions. Some dependence measures as Kendall’s tau and median regression model are discussed. After introducing the general class, four special sub models of the new family are introduced by taking the baseline distributions as Pareto, inverted Topp-Leone, exponential, and Rayleigh distributions. Maximum likelihood and Bayesian approaches are used to estimate the model unknown parameters. Further, percentile bootstrap confidence interval and bootstrap-t confidence interval are estimated for the model’s parameters. A Monte-Carlo simulation study is carried out of the maximum likelihood and Bayesian estimators. Finally, we illustrate the importance of the proposed bivariate family using two real data sets in medical field.

Bootstrap simulations for evaluating the model estimation of the extent of cross-pollination in maize at the field-scale level.

With the recent advent of genetic engineering, numerous genetically modified (GM) crops have been developed, and field planting has been initiated. In open-environment cultivation, the cross-pollination (CP) of GM crops with wild relatives, conventional crops, and organic crops can occur. This exchange of genetic material results in the gene flow phenomenon. Consequently, studies of gene flow among GM crops have primarily focused on the extent of CP between the pollen source plot and the adjacent recipient field. In the present study, Black Pearl Waxy Corn (a variety of purple glutinous maize) was used to simulate a GM-maize pollen source. The pollen recipient was Tainan No. 23 Corn (a variety of white glutinous maize). The CP rate (%) was calculated according to the xenia effect on kernel color. We assessed the suitability of common empirical models of pollen-mediated gene flow (PMGF) for GM maize, and the field border (FB) effect of the model was considered for small-scale farming systems in Asia. Field-scale data were used to construct an optimal model for maize PMGF in the maize-producing areas of Chiayi County, southern Taiwan (R.O.C). Moreover, each model was verified through simulation and by using the 95% percentile bootstrap confidence interval length. According to the results, a model incorporating both the distance from the source and the FB can have optimal fitting and predictive abilities.

Inference based on partly interval censored data from a two-parameter Rayleigh distribution

In this paper, the maximum likelihood and Bayesian estimation of the parameters of location-scale Rayleigh distribution with partly interval censored data is considered. For computing the maximum likelihood estimators with partly interval censored data, three methods are used, namely, Newton-Raphson, Expectation-Maximization and Monte-Carlo Expectation-Maximization algorithms. The standard errors of the estimates are computed using the observed information matrix. Also, two types of confidence intervals are constructed using the Wald method and the nonparametric percentile bootstrap confidence intervals. For computing the Bayes estimators, three methods viz Lindley's approximation, Tierney-Kadane approximation and importance sampling methods are used. Highest posterior density (HPD) credible intervals of the two parameters are constructed using importance sampling technique. Monte-Carlo simulation experiments are conducted to investigate the performance of the proposed methods. Finally, the methods are illustrated by using two real data sets, one is related with diabetic patients data set and the other is related to HIV infection data set.

Confidence intervals for the trade cost parameters of cross-section gravity models

Confidence intervals using robust PPML-standard errors are too small in cross-section gravity models. Monte Carlo simulations indicate approximately correct coverage rates of jackknife and percentile bootstrap confidence intervals. Those of constrained PPML estimates are reliable, if trade costs are non-stochastic.

Statistical inference for dependent stress–strength reliability of multi-state system using generalized survival signature

In reliability analysis of the stress–strength models, it is generally assumed that an individual only has one type of strength. However, in some situation, an individual, which has several types of independent or dependent strengths, is subjected several types of independent stresses in the working environment. Hence, we define a new multi-state stress–strength model for multi-state system consisting of n multi-state components with several types of strengths. In this paper, we discuss inferential procedures for stress–strength reliability of such multi-state system using generalized survival signature in two cases, viz., independent strengths and dependent strengths. Based on the assumption that the strengths and stresses variables follow exponential distributions, the exact expressions for stress–strength reliability of system in different states are derived in case of independent strengths. When the strengths are dependent, we utilize the Gumbel copula to depict the dependence structure of strengths. Additionally, two semiparametric methods, viz., method-of-moment and maximum pseudo-likelihood estimation, are used to estimate the dependence parameter. Then, maximum likelihood estimation, asymptotic confidence interval estimation and bootstrap percentile confidence interval estimation based on the aforementioned two semiparametric methods for the dependence parameter are provided, separately, to estimate the stress–strength reliability of system in different states. Monte Carlo simulations are performed to compare the performances of the proposed estimation methods. Finally, a real data analysis is provided to illustrate the proposed procedures.

A New Transmuted Generalized Lomax Distribution: Properties and Applications to COVID-19 Data.

A new five-parameter transmuted generalization of the Lomax distribution (TGL) is introduced in this study which is more flexible than current distributions and has become the latest distribution theory trend. Transmuted generalization of Lomax distribution is the name given to the new model. This model includes some previously unknown distributions. The proposed distribution's structural features, closed forms for an rth moment and incomplete moments, quantile, and Rényi entropy, among other things, are deduced. Maximum likelihood estimate based on complete and Type-II censored data is used to derive the new distribution's parameter estimators. The percentile bootstrap and bootstrap-t confidence intervals for unknown parameters are introduced. Monte Carlo simulation research is discussed in order to estimate the characteristics of the proposed distribution using point and interval estimation. Other competitive models are compared to a novel TGL. The utility of the new model is demonstrated using two COVID-19 real-world data sets from France and the United Kingdom.

Stress-Strength Reliability for Exponentiated Inverted Weibull Distribution with Application on Breaking of Jute Fiber and Carbon Fibers.

For the first time and by using an entire sample, we discussed the estimation of the unknown parameters θ1, θ2, and β and the system of stress-strength reliability R=P(Y < X) for exponentiated inverted Weibull (EIW) distributions with an equivalent scale parameter supported eight methods. We will use maximum likelihood method, maximum product of spacing estimation (MPSE), minimum spacing absolute-log distance estimation (MSALDE), least square estimation (LSE), weighted least square estimation (WLSE), method of Cramér-von Mises estimation (CME), and Anderson-Darling estimation (ADE) when X and Y are two independent a scaled exponentiated inverted Weibull (EIW) distribution. Percentile bootstrap and bias-corrected percentile bootstrap confidence intervals are introduced. To pick the better method of estimation, we used the Monte Carlo simulation study for comparing the efficiency of the various estimators suggested using mean square error and interval length criterion. From cases of samples, we discovered that the results of the maximum product of spacing method are more competitive than those of the other methods. A two real‐life data sets are represented demonstrating how the applicability of the methodologies proposed in real phenomena.

Orthorexic eating in women who are physically active in sport: A test of an objectification theory model

The present study tested an objectification model of orthorexic eating (i.e., pattern of disordered eating characterized by a preoccupation with food quality and purity, ritualistic styles of eating, avoidance of foods considered unhealthy, and compulsive evaluation of the source and nutritional content of foods) in women who are physically active in sport. A community sample of women physically active in sport (N = 228) completed an online survey with measures of orthorexic eating, phenomenological and appearance-based self-objectification, phenomenological body shame, sport-based perfectionism, and athletic identity. Parallel mediation analysis (PROCESS; Model 4) demonstrated a significant indirect effect (ab) of phenomenological self-objectification on orthorexic eating through phenomenological body shame (b = -0.10, 95 % percentile bootstrap confidence interval (PB CI): -0.15, −0.06), adjusting for three covariate measures. The direct effect (c′) of phenomenological self-objectification on orthorexic eating was not significant (b = 0.02, 95 % PB CI: -0.07, 0.10). The findings support an objectification theory model of orthorexic eating in community women who participate in sport.

A hybrid neural network for predicting Days on Market a measure of liquidity in real estate industry

In the real estate industry, Days on Market (DOM) is one of the most important attribute that is normally used to appraise real estate properties. In the academic sector, DOM is seemingly attracting a lot of researchers. DOM can be define as the length of time (i.e. in days) a real estate listing takes in passive market. In our paper, a novel hybrid neural network model is proposed to solve DOM prediction problem. Our proposed model extracts features using both CNN-based Attention (CNNA), and Bidirectional LSTM (BLSTM) modules. Furthermore, we concatenate their outputs and pass the results through a prediction (MLP) block, for predictions to be made. In implementing our model, overfitting was experienced as a challenge. In order to combat overfitting in our network we introduce Dropout layers in almost all the modules. Moreover, we present confidence intervals for four attributes in our dataset by using either percentile bootstrap confidence interval (CI) or percentile bias corrected accelerated (BCa) bootstrap CI, depending on the estimated distribution of an attribute. Finally, we appraise our model by experimenting with dataset of a famous real estate agency in Shanghai. The experimental outcomes clearly prove the superiority of the projected approach for solving DOM prediction problem.

Self-compassion and Body Checking Among Women: the Mediating Role of Stigmatizing Self-perceptions

The present study examined self-compassion and fear of self-compassion in relation to body checking behavior, an understudied marker of eating disorder risk. Stigmatizing self-perceptions in the form of self-objectification, body shame, and weight bias internalization were also examined as a collective set of factors that may connect self-compassion to body checking behavior. Undergraduate women (N = 177) recruited from a university participation pool completed the Self-Compassion Scale, the Fear of Self-Compassion Scale, the Weight Bias Internalization Scale—Modified, the Self-Objectification Beliefs and Behaviors Scale, the Phenomenological Body Shame Scale, and the Body Checking Scale in counterbalanced order via an online survey hosted on Qualtrics for course credit. Parallel mediation analyses conducted via PROCESS Model 4 demonstrated a significant indirect effect of self-compassion on body checking through weight bias internalization (ab = − .20; 95% percentile bootstrap confidence interval (PB CI): − .31 to − .11), self-objectification (ab = − .15; 95% PB CI: − .25 to − .08), and body shame (ab = − .16; 95% PB CI: − .28 to − .05). Significant indirect effects of fear of self-compassion on body checking were also demonstrated through weight bias internalization (ab = .18; 95% PB CI: .10 to .26), self-objectification (ab = .13; 95% PB CI: .07 to .21), and body shame (ab = .12; 95% PB CI: .03 to .21). No direct effects of self-compassion were observed. The findings support self-stigma as a psychological bridge that links being self-compassionate to body checking behavior. Low self-compassion and fear of self-compassion may allow stigmatizing self-perceptions to take root and drive more body checking behavior.