Variance Estimation Research Articles

Cardiorespiratory fitness, body mass index and mortality: a systematic review and meta-analysis

ObjectiveThe purpose of this review was to assess the joint relationship of cardiorespiratory fitness (CRF) and Body Mass Index (BMI) on both cardiovascular disease (CVD) and all-cause mortality risk.DesignA systematic review and meta-analysis was conducted. Pooled HR and 95% CI were calculated using a three-level restricted maximum likelihood estimation random-effects model with robust variance estimation. The reference group was normal weight-fit and was compared with normal weight-unfit, overweight-unfit and fit, and obese-unfit and fit.Data sourcesElectronic databases (PubMed/MEDLINE, Web of Science and SportDiscus) were searched following registration on PROSPERO.Eligibility criteriaArticles meeting the following criteria were included: (1) published between January 1980 and February 2023, (2) prospective cohort study, (3) CRF assessed using a maximal or VO2peak exercise test, (4) BMI reported and directly measured, (5) joint impact of CRF and BMI on all-cause mortality or CVD mortality were analysed, and (6) the reference group was normal weight, fit individuals.Results20 articles were included in the analysis resulting in a total of 398 716 observations. Compared with the reference group, overweight-fit (CVD HR (95% CI): 1.50 (0.82–2.76), all-cause HR: 0.96 (0.61–1.50)) and obese-fit (CVD: 1.62 (0.87–3.01), all-cause: 1.11 (0.88–1.40)) did not have a statistically different risk of mortality. Normal weight-unfit (CVD: 2.04 (1.32–3.14), all-cause: 1.92 (1.43–2.57)), overweight-unfit (CVD: 2.58 (1.48–4.52), all-cause: 1.82 (1.47–2.24)) and obese-unfit (CVD: 3.35 (1.17–9.61), all-cause: 2.04 (1.54–2.71)) demonstrated 2–3-fold greater mortality risks.ConclusionsCRF is a strong predictor of CVD and all-cause mortality and attenuates risks associated with overweight and obesity. These data have implications for public health and risk mitigation strategies.

Enhanced Wavelet Scattering Network for Image Inpainting Detection

The rapid advancement of image inpainting tools, especially those aimed at removing artifacts, has made digital image manipulation alarmingly accessible. This paper proposes several innovative ideas for detecting inpainting forgeries based on a low-level noise analysis by combining Dual-Tree Complex Wavelet Transform (DT-CWT) for feature extraction with convolutional neural networks (CNN) for forged area detection and localization, and lastly by employing an innovative combination of texture segmentation with noise variance estimations. The DT-CWT offers significant advantages due to its shift-invariance, enhancing its robustness against subtle manipulations during the inpainting process. Furthermore, its directional selectivity allows for the detection of subtle artifacts introduced by inpainting within specific frequency bands and orientations. Various neural network architectures were evaluated and proposed. Lastly, we propose a fusion detection module that combines texture analysis with noise variance estimation to give the forged area. Also, to address the limitations of existing inpainting datasets, particularly their lack of clear separation between inpainted regions and removed objects—which can inadvertently favor detection—we introduced a new dataset named the Real Inpainting Detection Dataset. Our approach was benchmarked against state-of-the-art methods and demonstrated superior performance over all cited alternatives.

Kawí Tamiruyé: a permanent research forest plot in the Sierra Tarahumara

This study characterizes the dasometric and structural parameters of a permanent research plot in northern Mexico. From a total census, 2165 trees (n) were recorded in an area of 1.4 ha-1 and, through the application of various sampling schemes, dasometric variables, and diversity indices were estimated for comparison with the census of a reference plot. We calculated the following dasometric measurements, Quadratic Mean Diameter (QMD)= 12.16 cm, Crown Area (CA)= 61.6%, Reineke's Stand Density Index (SDI)= 668 individuals, Basal Area (BA)= 25.16 m2, Total Volume (Vol)= 158.83 m3 ha-1, aboveground biomass (AGB= 142.78 Mg ha-1) and Carbon (C= 71.39 Mg ha-1). Ecologically, the species with the highest importance value index (IVI) were P. engelmannii and J. deppeana (50.8% and 35%, respectively). The Simpson's dominance index (δ)= 0.39 and diversity (λ)= 0.61 were low, as was the Shannon-Weaver index of diversity (H'= 1.3), which is in agreement with the Margalef index (DMg= 1.04). The value of Pielou's evenness (J'= 0.53) confirmed low abundance. Equidistant systematic sampling at 40 m was closer to the reference census, presenting lower values in the mean and variance estimators compared to the other sampling methods. The results can be included in management portfolios to improve decision-making. Although further studies on site size, inclusion of additive indices, and operational costs are required.

Genetic variation in surface temperature measured using infra-red thermography and genetic associations with production traits in grazing dairy cattle

ABSTRACT Increasing global temperatures and the incidence of extreme weather conditions will result in heat stress becoming a greater issue in production animals. Genetic selection and breeding for heat-tolerant animals have been promoted as a possible mitigation strategy in dairy cattle. The objectives of this study were to obtain in-field skin temperature measurements of the eye, muzzle and udder using infra-red thermography to examine the genetic variation in skin temperature within cows of a dairy herd and to estimate the genetic correlations between skin temperature and production traits. Thermal images and herd test records were obtained for the dairy herd at Massey University’s dairy farm 1. Estimates of (co)variances were obtained using the JWAS program with univariate and bivariate animal models. The heritability estimates for the eye, muzzle and udder temperature were low to moderate at 0.20, 0.24 and 0.39, respectively. All genetic correlations between production and temperature traits were positive except for eye temperature with milk yield and protein yield which was negative and weak. These results indicate that it may be possible to select for a greater skin temperature, however, these results need to be validated using a larger sample size.

New Modified Efficient Variance Estimators for the Estimation of Population Variance in Survey Sampling

For efficient and precise estimate of population variance, we have proposed some new ratio type variance estimators in the current research work by incorporating the linear combination of conventional and non-conventional parameters of auxiliary information. Bias and mean square error have been computed up to the first order of approximation. Numerical demonstration has been carried out to test the efficiency of new proposed estimators against existing estimators.

Efficient Handling of Non-response in Sample Surveys: Development of Ratio-type Variance Estimators

The present study has been under taken to address the issue of presence and absence of non-response problems in Sample surveys. We have developed some new efficient ratio-type variance estimators for the computation of the finite population variance in presence and absence of non-response, by incorporating Positional parameters as auxiliary information. The expression for mean square errors of proposed estimators has been derived up to the first order of approximation. To test the efficiency of new developed estimators, practical demonstration has been carried to ascertain the performance of suggested Estimators.

Mean Variance Estimation Neural Network Particle Filter for Predicting Battery Remaining Useful Life

Traditional remaining useful life (RUL) prediction methods based on particle filter (PF) require the manual tuning of hyperparameters, such as process or measurement noise, which poses challenges, particularly in real-life applications where external and operating conditions may change, potentially leading to large errors in the predictions. We address this issue by replacing the measurement equation of a PF with a mean variance estimation neural network that estimates the mean and the variance of the output distribution. As a result, the measurement noise is automatically estimated by the neural network and does not require manual setting. Through simulations and comparative analyses with state-of-the-art methods, the proposed mean variance estimation neural network particle filter (MVENN-PF) is shown to provide more stable and accurate RUL predictions, thereby potentially enhancing the robustness of battery health management systems based on it. Additionally, by eliminating the need to manually set a model hyperparameter (the measurement noise) the proposed method simplifies the modeling process, making it more accessible and adaptable to various battery systems.

Variance estimation using memory type estimators based on EWMA statistic for time scaled surveys in stratified sampling

In this article, we have proposed memory-type exponential and non-exponential estimators for population variance based on exponentially weighted moving average (EWMA) statistic in stratified sampling. We drive mathematical expressions for mean square errors of the proposed estimators using Taylor and exponential expansions. Our analysis demonstrates that the proposed memory-type estimators outperform the conventional estimators under stratification, particularly, when the information of previous sample is utilized. The performances of the proposed estimators are evaluated mathematically by deriving the conditions in which the memory-type estimators would perform better than their corresponding conventional estimators under stratification. Through an extensive simulation, we evaluated the performance of the proposed estimators across various population parameters, revealing their enhanced efficiency in time-scaled survey. Additionally, a real data application is also used to support the mathematical findings, confirming the practical utility of the proposed estimators. The results of numerical study underscore the importance of the use of previous sampled information which significantly improves the accuracy and reliability of the proposed estimator for variance estimation for time-scaled surveys.

Empirical Sandwich Variance Estimator for Iterated Conditional Expectation g-Computation.

Iterated conditional expectation (ICE) g-computation is an estimation approach for addressing time-varying confounding for both longitudinal and time-to-event data. Unlike other g-computation implementations, ICE avoids the need to specify models for each time-varying covariate. For variance estimation, previous work has suggested the bootstrap. However, bootstrapping can be computationally intense. Here, we present ICE g-computation as a set of stacked estimating equations. Therefore, the variance for the ICE g-computation estimator can be consistently estimated using the empirical sandwich variance estimator. Performance of the variance estimator was evaluated empirically with a simulation study. The proposed approach is also demonstrated with an illustrative example on the effect of cigarette smoking on the prevalence of hypertension. In the simulation study, the empirical sandwich variance estimator appropriately estimated the variance. When comparing runtimes between the sandwich variance estimator and the bootstrap for the applied example, the sandwich estimator was substantially faster, even when bootstraps were run in parallel. The empirical sandwich variance estimator is a viable option for variance estimation with ICE g-computation.

Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach.

Sparse data bias, where there is a lack of sufficient cases, is a common problem in data analysis, particularly when studying rare binary outcomes. Although a two-step meta-analysis approach may be used to lessen the bias by combining the summary statistics to increase the number of cases from multiple studies, this method does not completely eliminate bias in effect estimation. In this paper, we propose a one-shot distributed algorithm for estimating relative risk using a modified Poisson regression for binary data, named ODAP-B. We evaluate the performance of our method through both simulation studies and real-world case analyses of postacute sequelae of SARS-CoV-2 infection in children using data from 184 501 children across eight national academic medical centers. Compared with the meta-analysis method, our method provides closer estimates of the relative risk for all outcomes considered including syndromic and systemic outcomes. Our method is communication-efficient and privacy-preserving, requiring only aggregated data to obtain relatively unbiased effect estimates compared with two-step meta-analysis methods. Overall, ODAP-B is an effective distributed learning algorithm for Poisson regression to study rare binary outcomes. The method provides inference on adjusted relative risk with a robust variance estimator.

Faecal biomarkers in children with coeliac disease: A way forward?

Novel markers to reflect the intestinal damage in coeliac disease are needed. We studied the potential of faecal and serum neopterin, and faecal myeloperoxidase, human β-defensin-2, and lipocalin-2 in a case-control study. Data were collected from medical records and a biobank including newly diagnosed coeliac disease patients, potential coeliac disease patients and non-coeliac controls. Commercially available ELISA assays were used for measuring the biomarkers. Altogether 19 patients with coeliac disease (median age 9.0 years), 8 with potential coeliac disease (4.0 years) and 18 controls (6.5 years) were included. The highest faecal neopterin levels were seen in potential coeliac disease, followed by controls and coeliac disease (median 513 vs. 372 vs. 255 nmol/L, respectively, p = 0.016). Also, serum neopterin was highest in the potential coeliac disease group (9.8 vs. 5.5 vs. 5.9 nmol/L, p = 0.022). After age adjustment and robust variance estimation, only differences in serum neopterin remained significant. Other markers did not differ between the groups. None of the markers were significantly associated with serum transglutaminase-2 antibody levels. Differences in neopterin levels amongst patients with potential coeliac disease, coeliac disease, and controls suggest that neopterin might serve as an early disease marker.

Causal Inference with Complex Surveys: A Unified Perspective on Sample Selection and Exposure Selection

Probability surveys are a major source of population representative data for policy research and program evaluation. However, the data come with the added complications of being observational and selected with unequal probabilities. Propensity score adjustments have become increasingly popular for inferring causal relationships in non-randomized studies, but when using survey data, estimates of the population level causal effect may be biased if the sampling design is not adequately adjusted for. The current practice of using propensity score estimators with complex surveys is somewhat ad-hoc. We propose a potential-outcome super-population framework to streamline the causal analysis. We also develop propensity-score-and-survey weighted estimators and corresponding variance estimators, as well as their asymptotic properties. Our framework clarifies the confusion regarding the use of survey weighted propensity score in practice. The choice actually depends on the available sampling weights. Various estimators are compared in a simulation study, which shows that the proposed estimators perform better than the competing methods in terms of bias and confidence interval coverage when treatment effects are heterogeneous. To address an important public health issue, we evaluate the impact of e-cigarette use on future tobacco use intention in teens, using a large nationally representative survey in the United States.

Effects of model-overfit on model-assisted forest inventory in boreal forests with remote sensing data

Abstract While remote sensing can be an effective tool in building a forest inventory, field measurements and model fitting can be both expensive and challenging. One strategy to reduce forest inventory costs is to leverage forest inventory models fitted to a different population (external models), although the effectiveness of external models is poorly understood. One concern is that models may predict well to the sample data, but poorly to the population—which is termed ‘overfitting’. The effect of overfit may be especially problematic in attempts to predict for a different population (a forest area not covered by any sample plots). Assessing overfit is difficult and its consequence for estimation are not well understood, especially in the context of prediction using external models. This study assesses how overfitting affects model-assisted forest inventory estimation when using internal and external models. We used field and remotely sensed data (Sentinel-2 images and airborne laser scanning data) from two forest areas in Finland. We evaluated four modeling approaches: ordinary least square regression (OLS), random forest, k-nearest neighbors, and gaussian process regression. Both analytical and bootstrap variance estimators were used to evaluate model-assisted estimation performance. Internal models, especially OLS, were the most affected by model overfitting, leading to bias in the population means and underestimation of variance. Estimates using external models provided unbiased means and realistic intervals except in the case of deliberate excessive overfitting. The bootstrap variance estimator was found to be more robust to overfit than the analytical variance estimator for the internal model, but was not helpful for the external model. Internal models should be parsimonious to generalize well to the population and avoid bias. The bootstrap estimator of variance is recommended for internal models, especially if there is concern about model overfitting.

English

Coffee breeding programs aim at developing superior genotypes, which entail exploring the genetic variability existing in conilon/robusta coffee. This study aimed at examining the variability and estimate genetic param of physical and physiological nature in Coffea canephora seeds in order to provide bases for the selection of genotypes for superior seeds quality. Forty-three C. canephora genotypes were evaluated for 11 physical and physiological seed traits. Estimates of genetic param, environmental variance, phenotypic variance, genotypic variance, broad-sense heritability, variation index and Pearson's correlation were obtained. The Dissimilarity between genotypes was obtained by generalized Mahalanobis distance. The dendrogram was constructed using unweighted pair group method with arithmetic mean (UPGMA). C. canephora genotypes presented high germination potential and exhibited genetic diversity for all seed traits, with estimates of genetic variance greater than environmental variance. Heritability reached values above 96.0% and all genetic variation indices found were higher than unity. The UPGMA hierarchical method grouped the genotypes in three groups. The physical traits (circumference, area and roundness) were those that contributed the most to explaining the dissimilarity between genotypes. Genotypes Alecrim, AP, Bamburral, Z39 and Tardio C were the most divergent. The results revealed the presence of genetic variability between C. canephora genotypes and greater genetic than environmental influence on the characters of seeds, indicating that selection for the studied traits showed favorable conditions in terms of immediate gain.

Who am I? A second-order meta-analytic review of correlates of the self in childhood and adolescence.

People's subjective beliefs about themselves affect what people think and, consequently, what they do. Positive self-beliefs are important for many life outcomes, from academic success to well-being, especially during K-12 education as a crucial developmental period. Many empirical studies and meta-analyses have examined correlates of self-beliefs. The present second-order meta-analytic review integrates this large and diverse body of research, addressing two research aims: First, we examined the comparative strength of different variables related to self-beliefs. Second, we provide a methodological review of meta-analyses in this area, thereby facilitating readers' ability to assess the risk of bias when interpreting the results. We summarized 105 first-order meta-analyses published before July 2023 that investigated variables associated with self-beliefs during K-12 education, comprising 493 first-order effect sizes based on more than 8,500 primary studies and more than 16 million children and adolescents. We computed second-order standardized mean differences (SMD) using two-level meta-analyses with robust variance estimation. Personal characteristics (SMD = 0.50) showed stronger relations with self-beliefs than interventions (SMD = 0.27). Achievement (SMD = 0.66) and noncognitive variables (SMD = 0.67) were the personal characteristics most strongly related to self-beliefs compared to cognitive abilities (SMD = 0.30) and background variables (SMD = 0.21). Interventions targeting individual characteristics (SMD = 0.35) and especially self-beliefs (SMD = 0.52) showed larger effect sizes than interventions that focused on improving teaching and classroom structure (SMD = 0.20). Few meta-analyses investigated situational aspects, such as the geographical origin of the sample, in association with children's and adolescents' self-beliefs. Overall, this second-order meta-analytic review provides a comprehensive map of correlates of the self, highlighting pathways for future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Bayesian “Sandwich” for Variance Estimation

A Bayesian “Sandwich” for Variance Estimation

Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes.

An informed estimate of subject-level variance is a key determinate for accurate estimation of the required sample size for clinical trials. Evaluating completed adult Type 2 diabetes studies submitted to the FDA for accuracy of the variance estimate at the planning stage provides insights to inform the sample size requirements for future studies. From the U.S. Food and Drug Administration (FDA) database of new drug applications containing 14,106 subjects from 26 phase 3 randomized studies submitted to the FDA in support of drug approvals in adult type 2 diabetes studies reviewed between 2013 and 2017, we obtained estimates of subject-level variance for the primary endpoint-change in glycated hemoglobin (HbA1c) from baseline to 6 months. In addition, we used nine additional studies to examine the impact of clinically meaningful covariates on residual standard deviation and sample size re-estimation. Our analyses show that reduced sample sizes can be used without interfering with the validity of efficacy results for adult type 2 diabetes drug trials. This finding has implications for future research involving the adult type 2 diabetes population, including the potential to reduce recruitment period length and improve the timeliness of results. Furthermore, our findings could be utilized in the design of future endocrinology clinical trials.

Note on the radical inflation in the estimates of error variance in measurement models

This note discusses the radical technical inflation in error variance and the related standard error of test scores from both conceptual and empirical viewpoints. This technical inflation arises as a direct consequence of the technical underestimation of item-score correlation by the product–moment coefficient of correlation (PMC), which is embedded in the traditional estimators of reliability such as coefficients alpha, theta, omega, or rho (maximal reliability). Specifically, in educational settings where compilations usually include both easy and difficult items, the estimate by PMC may substantially deviate from the true association between an item and the score. Consequently, the use of traditional estimators of reliability leads to technically inflated estimates of standard errors, as the error variance related to these traditional measurement models is significantly inflated, resulting in deflated reliability estimates. In educational testing, employing deflation-corrected standard errors, calculated using deflation-corrected reliability estimators, would provide a more accurate measure of the test score’s true precision.

Invariance Principle and Berry–Esseen Bound for Error Variance Estimator for Pth-Order Nonlinear Autoregressive Models

The invariance principle and Berry–Esseen bound for an error variance estimator based on the residuals are established by using a Taylor expansion and the classical invariance principle and Berry–Esseen bound for independent random variables. Some examples are given to illustrate their applications.

Association between SMOFlipid and impaired brain development on term-equivalent age brain magnetic resonance imaging in very preterm infants

Soybean oil, medium-chain triglycerides, olive oil, and fish oil (SMOFlipid) is used without evidence of benefits. We investigated the relationship between lipid emulsions and brain injury in term-equivalent age magnetic resonance imaging (MRI) in 148 very preterm infants with a birth weight of < 1500 g at ≤ 32 gestational weeks in a neonatal intensive care unit. Infants who received soybean-based lipid emulsions between January 2015 and December 2018 were compared with those who received SMOFlipids between January 2019 and December 2022. A negative binomial generalized linear model was applied for bivariate analysis. Modified log-Poisson regression with generalized linear models and a robust variance estimator (Huber–White) were applied to adjust for potential confounders. The Kidokoro score was used to determine if lipid emulsion type would affect brain morphology and growth at term-equivalent age. Eighty-six (58.9%) received SMOFlipid. SMOFlipid was associated with lower focal signal abnormality, myelination delay, increased extracerebral space, and cerebellar volume reduction (P = 0.02, P = 0.007, P = 0.01, P = 0.02, respectively). SMOFlipidis are associated with brain insult, especially in white matter, cortical gray matter, and the cerebellum. Well-designed studies are needed to investigate the effect of lipid emulsions on the central nervous system.