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To examine child-parent associations of HCT among Japanese adults and their parents. Factors associated with hematocrit (HCT) were analyzed in 3,574 sons and 7,203 daughters using Pearson's correlation coefficient and Student's t-test. Multiple linear regression analysis, adjusted by the factors identified by univariate analyses and by living with parents, was performed on 242 son-parent trios and 587 daughter-parent trios. When a child-parent association was observed in the multiple linear regression analysis, it was validated using the random family method (RFM). In univariate analyses, the son's HCT was associated with age (correlation coefficient = -0.072), white blood cell (WBC) (0.19), alanine aminotransferase (ALT) (0.20), triglyceride (0.11), and estimated glomerular filtration rate (eGFR) (- 0.087). The daughter's HCT was associated with WBC (0.014), ALT (0.18), and eGFR (- 0.17). In multiple linear regression analysis, the son's HCT was associated with the son's WBC (coefficient = 3.48 × 10-4), the son's eGFR (0.031), the father's HCT (0.11), and the mother's HCT (0.17). RFM confirmed the association between the son's and father's HCT (p = 0.0070) and between the son's and mother's HCT (p = 0.0011). The daughter's HCT was associated with WBC (2.6 × 10-4), ALT (0.037), and the mother's HCT (0.14). RFM confirmed the association between the daughter's and mother's HCT (p = 0.00043). Child-parent association of HCT was confirmed between son-father, son-mother, and daughter-mother relationships, and differed depending on the sex of the child and the parents.

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Due to their high bias in favor of the majority class, traditional machine learning classifiers face a great challenge when there is a class imbalance in biological data. More recently, generative adversarial networks (GANs) have been applied to imbalanced data classification. For GANs, the distribution of the minority class data fed into discriminator is unknown. The input to the generator is random noise ($ z $) drawn from a standard normal distribution $ N(0, 1) $. This method inevitably increases the training difficulty of the network and reduces the quality of the data generated. In order to solve this problem, we proposed a new oversampling algorithm by combining the Bootstrap method and the Wasserstein GAN Network (BM-WGAN). In our approach, the input to the generator network is the data ($ z $) drawn from the distribution of minority class estimated by the BM. The generator was used to synthesize minority class data when the network training is completed. Through the above steps, the generator model can learn the useful features from the minority class and generate realistic-looking minority class samples. The experimental results indicate that BM-WGAN improves the classification performance greatly compared to other oversampling algorithms. The BM-WGAN implementation is available at: https://github.com/ithbjgit1/BMWGAN.git.

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Gompertz distribution is a significant and commonly used lifetime distribution, which plays an important role in reliability engineering. In this paper, we study the statistical inference of Gompertz distribution based on adaptive Type-II hybrid progressive censored schemes. From the perspective of frequentist, we derive the point estimations through the method of maximum likelihood estimation (MLE) and the existence of MLE is proved. Besides MLE, we propose the stochastic EM algorithm to reduce complexity and simplify computing. We also apply the method of Bootstraps (Bootstrap-p and Bootstrap-t) to construct confidence intervals. From Bayesian aspect, the Bayes estimates of the unknown parameters are evaluated by applying the MCMC method, the average length and coverage rate of credible intervals are also carried out. The Bayes inference is based on the squared error loss function and LINEX loss function. Furthermore, a numerical simulation is conducted to assess the performance of the proposed methods. Finally, a real-life example is considered to illustrate the application and development of the inference methods. In summary, the Bayesian method seems to perform the best among all approaches, while other approaches also present different advantages.

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Green bonds offer substantial positive externalities compared to other types of bonds. This leads to a resource distribution efficiency that falls below the optimal level dictated by Pareto efficiency. It becomes essential to determine a means by which green bonds can achieve an equilibrium price, ensuring optimal public resource allocation and maximized social welfare. From the perspective of externalities, this study employs the carbon shadow price (CSP) to determine the equilibrium price of carbon emissions. Subsequently, this value aids in estimating the equilibrium price of green bonds. Firstly, we introduced an optimized bootstrap method to estimate the bias-corrected CSP at the provincial level in China from 2007 to 2020. Then, a pricing framework is developed, integrating both the carbon trading price and the estimated CSP, to determine the green bond's equilibrium price. Numerical simulations indicate that, under current conditions, green bonds cannot achieve the equilibrium price by relying solely on the carbon trading mechanism. Therefore, further development of China's carbon emissions trading market is required.

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Excessive iodine exposure can have detrimental effects on thyroid function and overall health. This study aimed to investigate the age-dependent association between high urinary iodine concentration (UIC) and major depression symptoms in adults, using data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2020. To perform stratified analysis by age, we utilized a rolling window method with a 15-year window width to examine the trend of the odds ratios (ORs) of UIC on depression symptoms with age. Full-factor and one-factor multinomial logistic regression models were employed to calculate the ORs, and violin plots were utilized to depict the ORs of UIC on major depression. The LASSO regression was applied to select variables for one-factor models. The bootstrap method was utilized to ensure the robustness of the results, and the Games-Howell test was applied to compare the differences in the bootstrapped ORs of different UIC groups. Our results indicate that, after age 46, the ORs of high UIC (≥ 300 µg/L) on major depression are significantly higher than those of normal UIC (100-199 µg/L). The bootstrapped ORs of high UIC on major depression calculated by the full-factor and one-factor multinomial logistic regression models are 1.9 (1.28, 2.82) and 1.42 (1.02, 1.93) among participants aged 46 and older, respectively. Based on these findings, we conclude that major depressive symptoms are significantly associated with high UIC among older individuals aged 46 and above.

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Meta-analysis combines pertinent information from existing studies to provide an overall estimate of population parameters/effect sizes, as well as to quantify and explain the differences between studies. However, testing between-study heterogeneity is one of the most challenging tasks in meta-analysis research. Existing methods for testing heterogeneity, such as the Q test and likelihood ratio (LR) test, have been criticized for their failure to control Type I error rate and/or failure to attain enough statistical power. Although better reference distribution approximations have been proposed in the literature, their application is limited. Additionally, when the interest is to test whether the size of the heterogeneity is larger than a specific level, existing methods are far from mature. To address these issues, we propose new heterogeneity tests. Specifically, we combine bootstrap methods with existing heterogeneity tests (i.e., the maximum LR test, the restricted maximum LR test, and the Q test) to overcome the reference distribution issue and denote them as B-ML-LRT, B-REML-LRT, and B-Q, respectively. Simulation studies were conducted to examine and compare the performance of the proposed methods with the regular LR test, the regular Q test, and the Kulinskaya's improved Q test in both random- and mixed-effects meta-analyses. Based on the results of Type I error rates and statistical power, B-REML-LRT is recommended. Additionally, the improved Q test is also recommended when it is applicable. An R package boot.heterogeneity is provided to facilitate the implementation of the proposed tests.

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Pathogenicity-associated genes are highly host-specific and contribute to host-specific virulence. We tailored the traditional Koch's postulates with integrative omics by hypothesizing that the effector genes associated with host-pathogenicity are determinant markers for virulence, and developed Integrative Pathogenicity (IP) postulates for authenticated pathogenicity testing in plants. To set the criteria, we experimented on datepalm (Phoenix dactylifera) for the vascular wilt pathogen and confirmed the pathogen based on secreted in xylem genes (effectors genes) using genomic and transcriptomic approaches, and found it a reliable solution when pathogenicity is in question. The genic regions ITS, TEF1-α, and RPBII of Fusarium isolates were examined by phylogenetic analysis to unveil the validated operational taxonomy at the species level. The hierarchical tree generated through phylogenetic analysis declared the fungal pathogen as Fusarium oxysporum. Moreover, the Fusarium isolates were investigated at the subspecies level by probing the IGS, TEF1-α, and Pgx4 genic regions to detect the forma specialis of F. oxysporum that causes wilt in datepalm. The phylogram revealed a new forma specialis in F. oxysporum that causes vascular wilt in datepalm.

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With advances in cancer treatments and improved patient survival, more patients may go through multiple lines of treatment. It is of clinical importance to choose a sequence of effective treatments (eg, lines of treatment) for individual patients with the goal of optimizing their long-term clinical outcome (eg, survival). Several important issues arise in cancer studies. First, cancer clinical trials are usually conducted by each line of treatment. For a treatment sequence, we may have first line and second line treatment data from two different studies. Second, there is typically a treatment initiation period varying from patient to patient between progression of disease and the start of the second line treatment due to administrative reasons. Additionally, the choice of the second line treatment for patients with progression of disease may depend on their characteristics. We address all these issues and develop semiparametric methods under the potential outcome framework for the estimation of the overall survival probability for a treatment sequence and for comparing different treatment sequences. We establish the large sample properties of the proposed inferential procedures. Simulation studies and an application to a colorectal clinical trial are provided.

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In this work we fit an epidemiological model SEIAQR (Susceptible - Exposed - Infectious - Asymptomatic - Quarantined - Removed) to the data of the first COVID-19 outbreak in Rio de Janeiro, Brazil. Particular emphasis is given to the unreported rate, that is, the proportion of infected individuals that is not detected by the health system. The evaluation of the parameters of the model is based on a combination of error-weighted least squares method and appropriate B-splines. The structural and practical identifiability is analyzed to support the feasibility and robustness of the parameters' estimation. We use the Bootstrap method to quantify the uncertainty of the estimates. For the outbreak of March-July 2020 in Rio de Janeiro, we estimate about 90% of unreported cases, with a 95% confidence interval (85%, 93%).

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Missing data are commonly encountered in clinical trials due to dropout or nonadherence to study procedures. In trials in which recurrent events are of interest, the observed count can be an undercount of the events if a patient drops out before the end of the study. In many applications, the data are not necessarily missing at random and it is often not possible to test the missing at random assumption. Consequently, it is critical to conduct sensitivity analysis. We develop a control-based multiple imputation method for recurrent events data, where patients who drop out of the study are assumed to have a similar response profile to those in the control group after dropping out. Specifically, we consider the copy reference approach and the jump to reference approach. We model the recurrent event data using a semiparametric proportional intensity frailty model with the baseline hazard function completely unspecified. We develop nonparametric maximum likelihood estimation and inference procedures. We then impute the missing data based on the large sample distribution of the resulting estimators. The variance estimation is corrected by a bootstrap procedure. Simulation studies demonstrate the proposed method performs well in practical settings. We provide applications to two clinical trials.

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In this paper, we study the statistical inference of the generalized inverted exponential distribution with the same scale parameter and various shape parameters based on joint progressively type-II censored data. The expectation maximization (EM) algorithm is applied to calculate the maximum likelihood estimates (MLEs) of the parameters. We obtain the observed information matrix based on the missing value principle. Interval estimations are computed by the bootstrap method. We provide Bayesian inference for the informative prior and the non-informative prior. The importance sampling technique is performed to derive the Bayesian estimates and credible intervals under the squared error loss function and the linex loss function, respectively. Eventually, we conduct the Monte Carlo simulation and real data analysis. Moreover, we consider the parameters that have order restrictions and provide the maximum likelihood estimates and Bayesian inference.

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Model checking for logistic regression with covariates missing at random is considered. Based on the ideas of Copas (1989) and Osius and Rojek (1992) and studies of Homser et al. (1997), proposed are the two-type goodness-of-fit tests, Pearson chi-squared and unweighted residual sum-of-squares tests, in which their test statistics are centralized by subtracting their estimated mean to be mean-zero-form test statistics via the inverse probability weighting (IPW) and nonparametric multiple imputation (MI) methods to solve the missing value problem. The asymptotic properties of these test statistics are established under the null hypothesis and some regularity conditions. The test statistics conducted by using the IPW and MI estimators are asymptotically equivalent. Proposed are the IPW method and two bootstrap re-sampling approaches for estimation of the variances of the proposed test statistics to solve the issue of underestimating their variances by the MI method of Rubin (1987). Simulation studies are carried out to assess the finite-sample power performances of these proposed tests. Two real data examples are used to illustrate the applicability of the proposed tests.

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Mediation models have been widely used in many disciplines to better understand the underlying processes between independent and dependent variables. Despite their popularity and importance, the appropriate sample sizes for estimating those models are not well known. Although several approaches (such as Monte Carlo methods) exist, applied researchers tend to use insufficient sample sizes to estimate their models of interest, which might result in unstable and inaccurate estimation of the model parameters including mediation effects. In the present study, sample size requirements were investigated for four frequently used mediation models: one simple mediation model and three complex mediation models. For each model, path and structural equation modeling approaches were examined, and partial and complete mediation conditions were considered. Both the percentile bootstrap method and the multivariate delta method were compared for testing mediation effects. A series of Monte Carlo simulations was conducted under various simulation conditions, including those concerning the level of effect sizes, the number of indicators, the magnitude of factor loadings, and the proportion of missing data. The results not only present practical and general guidelines for substantive researchers to determine minimum required sample sizes but also improve understanding of which factors are related to sample size requirements in mediation models.

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Background: Elderly people have increased risk factors for low serum vitamin D levels, which is worsened among the black race. Therefore, elderly Africans constitute a reference population for vitamin D study. Aim: The aim of this study was to establish the reference interval of serum 25-hydroxyvitamin D (25(OH)D) among an African elderly population. Methodology: This was a cross-sectional study of rural community dwellers in Enugu, south-eastern Nigeria aged 50 years and above, that satisfied the criteria of the reference population. Ethical approval and informed consent were obtained. Venous blood was collected from reference individuals and serum 25(OH)D was determined by enzyme-linked immunosorbent assay. Data were analysed using a non-parametric, bootstrap method to obtain the central 95% reference limits and 90% confidence intervals of the lower and upper limits of the reference interval respectively. Results: One hundred and twenty-four (62 males and 62 females) participants were recruited. The median (25th -75th percentile) of serum 25(OH)D was 56 (35 - 71) ng/ml. The 2.5th percentile defined the lower reference limit and it was 21 ng/ml with 90% confidence interval (20 - 23) ng/ml; while the 97.5th percentile defined the upper reference limit and it was 93 ng/ml with 90% confidence interval (90 - 98) ng/ml. Conclusion: The reference interval for serum 25(OH)D for the selected African elderly population in Enugu, Nigeria was determined to be 21 to 93 ng/ml.

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The purpose of this paper was to introduce five key techniques and the multi-directional decomposition methods of effect components in the analysis of causal mediation effects. The contents of the five key technologies were as follows： ① identification of causal mediation effect； ② regression method of causal mediation effect analysis； ③ maximum likelihood estimation； ④ estimation of total effect and various component effects； ⑤ estimation by bootstrap method. The multi-directional decomposition methods included 3 bidirectional decompositions， 2 three-directional decompositions and 1 four-directional decomposition. Through an example， a causal mediation effect analysis model including covariates and interaction terms was constructed with the help of SAS， bidirectional decomposition， three-directional decomposition and four-directional decomposition were carried out for the total effect in the causal mediation effect analysis， and the output results were explained.

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The point and interval estimations for the unknown parameters of an exponentiated half-logistic distribution based on adaptive type II progressive censoring are obtained in this article. At the beginning, the maximum likelihood estimators are derived. Afterward, the observed and expected Fisher's information matrix are obtained to construct the asymptotic confidence intervals. Meanwhile, the percentile bootstrap method and the bootstrap-t method are put forward for the establishment of confidence intervals. With respect to Bayesian estimation, the Lindley method is used under three different loss functions. The importance sampling method is also applied to calculate Bayesian estimates and construct corresponding highest posterior density (HPD) credible intervals. Finally, numerous simulation studies are conducted on the basis of Markov Chain Monte Carlo (MCMC) samples to contrast the performance of the estimations, and an authentic data set is analyzed for exemplifying intention.

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When analysts evaluate performance assessments, they often use modern measurement theory models to identify raters who frequently give ratings that are different from what would be expected, given the quality of the performance. To detect problematic scoring patterns, two rater fit statistics, the infit and outfit mean square error (MSE) statistics are routinely used. However, the interpretation of these statistics is not straightforward. A common practice is that researchers employ established rule-of-thumb critical values to interpret infit and outfit MSE statistics. Unfortunately, prior studies have shown that these rule-of-thumb values may not be appropriate in many empirical situations. Parametric bootstrapped critical values for infit and outfit MSE statistics provide a promising alternative approach to identifying item and person misfit in item response theory (IRT) analyses. However, researchers have not examined the performance of this approach for detecting rater misfit. In this study, we illustrate a bootstrap procedure that researchers can use to identify critical values for infit and outfit MSE statistics, and we used a simulation study to assess the false-positive and true-positive rates of these two statistics. We observed that the false-positive rates were highly inflated, and the true-positive rates were relatively low. Thus, we proposed an iterative parametric bootstrap procedure to overcome these limitations. The results indicated that using the iterative procedure to establish 95% critical values of infit and outfit MSE statistics had better-controlled false-positive rates and higher true-positive rates compared to using traditional parametric bootstrap procedure and rule-of-thumb critical values.

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In this study, the delta method was applied to estimate the standard errors of the true score equating when using the characteristic curve methods with the generalized partial credit model in test equating under the context of the common-item nonequivalent groups equating design. Simulation studies were further conducted to compare the performance of the delta method with that of the bootstrap method and the multiple imputation method. The results indicated that the standard errors produced by the delta method were very close to the criterion empirical standard errors as well as those yielded by the bootstrap method and the multiple imputation method under all the manipulated conditions.

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Although dimensional analysis suggests sound functional forms (FFs) to calculate longitudinal dispersion coefficient (Kx), no attempt has been made to quantify both reliability of the estimated Kx value and its sensitivity to variation of the FFs' parameters. This paper introduces a new index named bandwidths similarity factor (bws-factor) to quantify the reliability of FFs based on a rigorous analysis of distinct calibration datasets to tune the FFs. We modified the bootstrap approach to ensure that each resampled calibration dataset is representative of available datapoints in a rich, global database of tracer studies. The dimensionless Kx values were calculated by 200 FFs tuned with the generalized reduced gradient algorithm. Correlation coefficients for the tuned FFs varied from 0.60 to 0.98. The bws-factor ranged from 0.11 to 1.00, indicating poor reliability of FFs for Kx calculation, mainly due to different sources of error in the Kx calculation process. The calculated exponent of the river's aspect ratio varied over a wider range (i.e., -0.76 to 1.50) compared to that computed for the river's friction term (i.e., -0.56 to 0.87). Since Kx is used in combination with one-dimensional numerical models in water quality studies, poor reliability in its estimation can result in unrealistic concentrations being simulated by the models downstream of pollutant release into rivers.

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This work aims to explore the meta inference of mean, where data are being collected from different studies. Providing an accurate estimate of the mean from separate studies is a central aspect of the practical sciences. The meta inference of means and its statistical inference is so prevalent that it has received much attention. The problem is well recognized as pertinent to carrying out the meta inference of mean, and its statistical inference has got a lot of attention. This paper intends to explore the existing techniques for dealing with this issue, propose and study alternative methods to improve the estimation of a common mean, and review the theoretical inference. This paper uses numerical investigations that continue to provide good results to evaluate the proposed methods.

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