Variance Estimation Research Articles

PDE-Constrained Scale Optimization Selection for Feature Detection in Remote Sensing Image Matching

Feature detection and matching is the key technique for remote sensing image processing and related applications. In this paper, a PDE-constrained optimization model is proposed to determine the scale levels advantageous for feature detection. A variance estimation technique is introduced to treat the observation optical images polluted by additive zero-mean Gaussian noise and determine the parameter of a nonlinear scale space governed by the partial differential equation. Additive Operator Splitting is applied to efficiently solve the PDE constraint, and an iterative algorithm is proposed to approximate the optimal subset of the original scale level set. The selected levels are distributed more uniformly in the total variation sense and helpful for generating more accurate and robust feature points. The experimental results show that the proposed method can achieve about a 30% improvement in the number of correct matches with only a small increase in time cost.

Multiple Imputation of Race and Hispanic Ethnicity in National Surveillance Data for Chlamydia, Gonorrhea, and Syphilis.

Disease burden of sexually transmitted infections such as chlamydia, gonorrhea, and syphilis is often compared across age categories, sex categories, and race and ethnicity categories. Missing data may prevent researchers from accurately characterizing health disparities between populations. This article describes the methods used to impute race and Hispanic ethnicity in a large national surveillance data set. All US cases of chlamydia, gonorrhea, and syphilis (excluding congenital syphilis) reported through the National Notifiable Diseases Surveillance System from the year 2019 were included in the analyses. We used fully conditional specification to impute missing race and Hispanic ethnicity data. After imputation, reported case rates were calculated, by disease, for each race and Hispanic ethnicity category using Vintage 2019 Population and Housing Unit Estimates from the US Census. We then used case counts from subsets that contained only complete race and Hispanic ethnicity information to investigate if the confidence intervals from the multiply imputed data included the observed number of cases in each race and Hispanic ethnicity category. Among the 2,553,038 cases reported in 2019, race and Hispanic ethnicity were multiply imputed for 9% of syphilis cases, 22% of gonorrhea cases, and 33% of chlamydia cases. In the subset analyses, every nonzero rate of reported cases was contained within the confidence intervals that were calculated from multiply imputed data. Confidence intervals that account for the uncertainty of the predictions are an advantage of multiple imputation over complete-case analysis because a realistic variance estimate allows for valid hypothesis testing results.

Suicide and Intentional Self-harm Among Older Men Treated With 5-Alpha Reductase Inhibitor or Alpha-blockers for Benign Prostatic Hyperplasia

ObjectiveTo compare the risk of intentional self-harm (ISH) and suicide in older men using 5-α reductase inhibitors (5-ARIs) and alpha-blockers for benign prostatic hyperplasia (BPH). Observational research of older men with BPH suggested an increase in ISH with 5-ARI use compared with nonuse; we aimed to address potential confounding by indication with an active comparator reference group. MethodsUsing Medicare data linked to the National Death Index (NDI) from 2007-2016, we implemented a retrospective cohort design in males aged ≥65 years who initiated 5-ARI or alpha-blocker use for BPH. ISH was identified using ICD-9-CM and ICD-10-CM diagnosis codes. Suicides were identified through cause-of-death information from the NDI. We used inverse probability of treatment weighted Cox proportional hazards regression to compare time-to-event between treatment groups, with robust variance estimation. ResultsThe event rates for ISH and suicide, respectively, were 0.314 and 0.308 per 1000 person-years (PY) among 5-ARI users (n=181,675), and 0.364 and 0.382 per 1000PY among alpha-blocker users (n=850,476). For 5-ARI use relative to alpha-blocker use, hazard ratios (HRs) for ISH and suicide, respectively, were 0.88 (95% CI:0.62–1.25) and 0.82 (95% CI:0.54–1.24); for the composite outcome (non-fatal ISH or suicide), the HR was 0.88 (95% CI:0.66–1.16). Subgroup and sensitivity analyses supported these results. Conclusion5-ARI use was not associated with an increased risk for ISH or suicide compared to alpha-blocker use in older men with BPH. Study limitations included low event rates and potentially low sensitivity for ISH events.

Implications of accounting for marker-based population structure in the quantitative genetic evaluation of genetic parameters related to growth and wood properties in Norway spruce

BackgroundForest geneticists typically use provenances to account for population differences in their improvement schemes; however, the historical records of the imported materials might not be very precise or well-aligned with the genetic clusters derived from advanced molecular techniques. The main objective of this study was to assess the impact of marker-based population structure on genetic parameter estimates related to growth and wood properties and their trade-offs in Norway spruce, by either incorporating it as a fixed effect (model-B) or excluding it entirely from the analysis (model-A).ResultsOur results indicate that models incorporating population structure significantly reduce estimates of additive genetic variance, resulting in substantial reduction of narrow-sense heritability. However, these models considerably improve prediction accuracies. This was particularly significant for growth and solid-wood properties, which showed to have the highest population genetic differentiation (QST) among the studied traits. Additionally, although the pattern of correlations remained similar across the models, their magnitude was slightly lower for models that included population structure as a fixed effect. This suggests that selection, consistently performed within populations, might be less affected by unfavourable genetic correlations compared to mass selection conducted without pedigree restrictions.ConclusionWe conclude that the results of models properly accounting for population structure are more accurate and less biased compared to those neglecting this effect. This might have practical implications for breeders and forest managers where, decisions based on imprecise selections can pose a high risk to economic efficiency.

Association between switching to integrase strand transfer inhibitors and incident diabetes in people with HIV.

Integrase strand transfer inhibitors (INSTI) are associated with weight gain in people with HIV (PWH), but their impact on diabetes is unclear. We evaluated the association between switching from nonnucleoside reverse-transcriptase inhibitors (NNRTI) or protease inhibitors (PI) to INSTI and incident diabetes. Longitudinal cohort study. We included PWH aged ≥18 years from the Johns Hopkins HIV Clinical Cohort (2007-2023) without history of diabetes who had used NNRTI or PI for ≥180 days. We followed participants up to 10 years from HIV primary care visits where they switched to INSTI or continued NNRTI or PI. We estimated the hazard of incident diabetes associated with switching to INSTI using weighted Cox regression with robust variance estimator. We included 2075 PWH who attended 22 116 visits where they continued NNRTI or PI and 631 visits where they switched to INSTI. Switching to INSTI was associated with a weighted hazard ratio (wHR) of 1.11 [95% confidence interval (CI), 0.77-1.59] for incident diabetes. The association if no weight gain occurred during the first two years was not qualitatively different (wHR 1.22; 95% CI, 0.82-1.80). In a posthoc analysis, switching to INSTI conferred a significant wHR of 1.79 (95% CI, 1.13-2.84) for diabetes within the first two years but not after. Switching from NNRTI or PI to INSTI did not significantly increase overall diabetes incidence in PWH, although there may be elevated risk in the first two years. These findings can inform considerations when switching to INSTI-based regimens.

Synthesizing Subject-matter Expertise for Variable Selection in Causal Effect Estimation: A Case Study.

Causal graphs are an important tool for covariate selection but there is limited applied research on how best to create them. Here, we used data from the Coronary Drug Project trial to assess a range of approaches to directed acyclic graph (DAG) creation. We focused on the effect of adherence on mortality in the placebo arm, since the true causal effect is believed with a high degree of certainty. We created DAGs for the effect of placebo adherence on mortality using different approaches for identifying variables and links to include or exclude. For each DAG, we identified minimal adjustment sets of covariates for estimating our causal effect of interest and applied these to analyses of the Coronary Drug Project data. When we used only baseline covariate values to estimate the cumulative effect of placebo adherence on mortality, all adjustment sets performed similarly. The specific choice of covariates had minimal effect on these (biased) point estimates, but including nonconfounding prognostic factors resulted in smaller variance estimates. When we additionally adjusted for time-varying covariates of adherence using inverse probability weighting, covariates identified from the DAG created by focusing on prognostic factors performed best. Theoretical advice on covariate selection suggests that including prognostic factors that are not exposure predictors can reduce variance without increasing bias. In contrast, for exposure predictors that are not prognostic factors, inclusion may result in less bias control. Our results empirically confirm this advice. We recommend that hand-creating DAGs begin with the identification of all potential outcome prognostic factors.

Ranking Search Results Based on User Preferences in the Absence of Personalized Statistics

The article considers the algorithm of search results ranking according to the compliance with user's expectations due to the feedback on the data of search results pre-release, which allowed to reduce the time of search for the necessary information. A numerical experiment aimed at evaluating the effectiveness of the proposed approach on the example of real estate objects is presented. Aggregated data of real estate listings were used as data, and the results of user surveys were used to evaluate the relevance. TOPSIS and PROMETHEE methods were used as pre-ranking algorithms. The ranking results were combined considering their order in the output of both methods. To implement the next step of the algorithm, the pre-release list was partitioned into classes for which users selected a few objects they liked. Machine learning models were trained on the partitioned data. Based on their average accuracy and variance estimates, a naive Bayesian classifier model was selected and used for subsequent computations. The results of further experiments showed the possibility of taking into account personal preferences when organizing search and selection of objects of interest in the absence of personalized statistics on the example of real estate objects. As a result of the experiments, the time of searching a group of results of interest was reduced by 74 % on average.

Maximum correntropy unscented filter based on unbiased minimum-variance estimation for a class of nonlinear systems

Introduction: The unscented Kalman filter based on unbiased minimum-variance (UKF-UMV) estimation is usually used to handle the state estimation problem of nonlinear systems with an unknown input. When the nonlinear system is disturbed by non-Gaussian noise, the performance of UKF-UMV will seriously deteriorate.Methods: A maximum correntropy unscented filter based on the unbiased minimum variance (MCUF-UMV) estimation method is proposed on the basis of the UKF-UMV without the need for estimation of an unknown input and uses the maximum correntropy criterion (MCC) and fixed-point iterative algorithm for state estimation.Results: When the measurement noise of the nonlinear system is non-Gaussian noise, the algorithm performs well.Discussion: Our proposed algorithm also does not require estimation of an unknown input, and there is no prior knowledge available about the unknown input or any prior assumptions. The unknown input can be any signal. Finally, a simulation example is used to demonstrate the effectiveness and reliability of the algorithm.

The impact of transformations on the performance of variance estimators of finite population under adaptive cluster sampling with application to ecological data

This paper aims to investigate the impact of transformed auxiliary variables on the performance of variance estimators of finite population under adaptive cluster sampling scheme. Further, the formulation of an efficient variance estimator of a finite population is also under consideration in this article. Specifically, we explore the gain in efficiency obtained through various transformations and define dominance space for each transformation. These dominance regions provide valuable insights into the circumstances under which one transformation prevails over another regarding precision and accuracy. The theoretical properties of the suggested estimators have been discussed along with the dominance region under each transformation. The bias and Mean Square Error (MSE) have been derived up to the first order of approximation. To evaluate and empirically validate our methodology, we conduct a numerical analysis using real-life ecological data of blue-winged teal. The finding reflects the superior performance of the suggested variance estimators over the competing estimators, thereby substantiating its importance in making informed decisions in real-world applications.

A family of estimators for variance estimation

A family of estimators for variance estimation

Is Phonological-Only Instruction Helpful for Reading?: A Meta-Analysis

ABSTRACT Purpose Phonological awareness (PA), the awareness of sounds in spoken words, is strongly linked to reading outcomes. However, there is an ongoing debate regarding the effectiveness of PA instruction without including print (i.e. PA without exposure to words or letters). Specifically, is PA-only instruction just as effective in improving reading outcomes when compared to print-based instruction (e.g. phonics)? Method Robust variance estimation was used to examine the effectiveness of PA-only instruction across 38 independent studies and 293 effect sizes. The sample consisted of 3,880 participants in preschool through first grade. The average age of participants was 5 years 7 months. Moderator analysis were conducted to examine differences in effect across outcomes, comparison conditions and participant characteristics. Results PA-only instruction had a moderate overall effect (g = 0.57, 95% CI [0.40, 0.74], p < .001) on reading-related outcomes. PA-only instruction was less effective when compared to print-based instruction (g = −0.54, 95% CI [−0.80, −0.27], p < .001) and less effective for word reading outcomes (g = -0.33, 95% CI [−0.66, −0.01], p = .05) than for PA outcomes (g = 0.64, 95% CI [0.44, 0.84], p < .001). Conclusion PA-only instruction can be beneficial for reading-related outcomes, but the effect varies depending on two important contextual factors: 1) the reading outcome type and 2) the comparison condition. Specifically, PA-only instruction might be less effective in improving word reading outcomes than PA outcomes. In addition, it has less of an effect on reading-related outcomes when compared to print-based instruction like phonics.

Prewhitened long-run variance estimation robust to nonstationarity

We introduce a nonparametric nonlinear VAR prewhitened long-run variance (LRV) estimator for the construction of standard errors robust to autocorrelation and heteroskedasticity that can be used for hypothesis testing in a variety of contexts including the linear regression model. Existing methods either are theoretically valid only under stationarity and have poor finite-sample properties under nonstationarity (i.e., fixed-b methods), or are theoretically valid under the null hypothesis but lead to tests that are not consistent under nonstationary alternative hypothesis (i.e., both fixed-b and traditional HAC estimators). The proposed estimator accounts explicitly for nonstationarity, unlike previous prewhitened procedures which are known to be unreliable, and leads to tests with accurate null rejection rates and good monotonic power. We also establish MSE bounds for LRV estimation that are sharper than previously established and use them to determine the data-dependent bandwidths.

Optimal training of Mean Variance Estimation neural networks

This paper focusses on the optimal implementation of a Mean Variance Estimation network (MVE network) (Nix and Weigend, 1994). This type of network is often used as a building block for uncertainty estimation methods in a regression setting, for instance Concrete dropout (Gal et al., 2017) and Deep Ensembles (Lakshminarayanan et al., 2017). Specifically, an MVE network assumes that the data is produced from a normal distribution with a mean function and variance function. The MVE network outputs a mean and variance estimate and optimizes the network parameters by minimizing the negative loglikelihood.In our paper, we present two significant insights. Firstly, the convergence difficulties reported in recent work can be relatively easily prevented by following the simple yet often overlooked recommendation from the original authors that a warm-up period should be used. During this period, only the mean is optimized with a fixed variance. We demonstrate the effectiveness of this step through experimentation, highlighting that it should be standard practice. As a sidenote, we examine whether, after the warm-up, it is beneficial to fix the mean while optimizing the variance or to optimize both simultaneously. Here, we do not observe a substantial difference. Secondly, we introduce a novel improvement of the MVE network: separate regularization of the mean and the variance estimate. We demonstrate, both on toy examples, multiple benchmark UCI regression data sets, and on the UTKFace data set, that following the original recommendations and the novel separate regularization can lead to significant improvements.

Treatments for undefined log ratios in matching analyses.

A challenge in carrying out matching analyses is to deal with undefined log ratios. If any reinforcer or response rate equals zero, the logarithm of the ratio is undefined: data are unsuitable for analyses. There have been some tentative solutions, but they had not been thoroughly investigated. The purpose of this article is to assess the adequacy of five treatments: omit undefined ratios, use full information maximum likelihood, replace undefined ratios by the mean divided by 100, replace them by a constant 1/10, and add theconstant .50 to ratios. Based on simulations, the treatments are compared on their estimations of variance accounted for, sensitivity, and bias. The results show that full information maximum likelihood and omiting undefined ratios had the best overall performance, with negligibly biased and more accurate estimates than mean divided by 100, constant 1/10, and constant .50. The study suggests that mean divided by 100, constant 1/10, and constant .50 should be avoided and recommends full information maximum likelihood to deal with undefined log ratios in matching analyses.

Professional Development in Reading Comprehension: A Meta‐analysis of the Effects on Teachers and Students

AbstractReading comprehension instruction was identified by the National Reading Panel as an effective instructional practice to improve students' reading comprehension. Teacher professional development (PD) is essential for effective reading comprehension instruction to occur in schools. The aim of this meta‐analysis is to examine the extent to which teacher PD in reading comprehension impacted teacher knowledge or practice outcomes and student reading comprehension outcomes. Additionally, we explored whether various studies or PD characteristics were potential moderators of these outcomes. We identified 29 experimental and quasi‐experimental studies, including 51 independent samples and 97 effect sizes. Using robust variance estimation, teacher PD in reading comprehension had a large effect (g = 0.947, p &lt; .001) on teacher knowledge or practice and a small effect (g = 0.193, p &lt; .001) on student reading comprehension. Results indicate teacher PD in reading comprehension is effective in improving teaching practices and student learning. However, more systematic research is needed to investigate effective PD components and other factors which may influence the impact of PD on teacher and student outcomes.

Double Exponential Ratio Estimator of a Finite Population Variance under Extreme Values in Simple Random Sampling

This article presents an improved class of efficient estimators aimed at estimating the finite population variance of the study variable. These estimators are especially useful when we have information about the minimum/maximum values of the auxiliary variable within a framework of simple random sampling. The characteristics of the proposed class of estimators, including bias and mean squared error (MSE) under simple random sampling are derived through a first-order approximation. To assess the performance and validate the theoretical outcomes, we conduct a simulation study. Results indicate that the proposed class of estimators has lower MSEs as compared to other existing estimators across all simulation scenarios. Three datasets are used in the application section to emphasize the effectiveness of the proposed class of estimators over conventional unbiased variance estimators, ratio and regression estimators, and other existing estimators.

Fear of COVID-19 associated with burnout syndrome in dentists of the Health Directorate of the National Police of Peru: a cross-sectional study at national level under multivariable regression model

BackgroundDuring the pandemic, many police dentists had the crucial responsibility of ensuring law and order while providing dental care by taking government-approved health measures to stop the spread of the coronavirus. The aim of this study was to assess the association between the fear of COVID-19 and Burnout syndrome in Peruvian dentists belonging to the Health Department of the National Police of Peru (PNP), taking into account possible confounding variables.MethodsThis cross-sectional and analytical study included 182 PNP dentists. The Fear COVID-19 Scale assessed fear of COVID-19 and the Maslach Burnout Inventory Test assessed burnout syndrome. The association between the fear of COVID-19 and Burnout syndrome (self-fulfilment) was analyzed using Spearman's Rho. A multivariable Poisson regression model with a robust variance estimation method was employed to evaluate the impact of fear of COVID-19 on the various dimensions of Burnout syndrome, considering possible confounding variables. The statistical significance level was set at p < 0.05.ResultsUnder bivariate analysis, fear of COVID-19 was significantly linked with low direct intensity toward emotional exhaustion (Rho = 0.325, p < 0.001), very low direct intensity toward depersonalization (Rho = 0.180, p = 0.015), and very low inverse intensity toward self-fulfilment (Rho =—0.186, p = 0.012). Under multivariable analysis, it was observed that dentists who exhibited fear of COVID-19 were 3.4 and 3.7 times more likely to experience emotional exhaustion and depersonalization, respectively (APR = 3.40, 95% CI: 1.74—6.63 and APR = 3.68, 95% CI: 1.31—10.37), as compared to those who did not display fear of COVID-19. Moreover, none of the potential confounding factors were found to have a significant impact on emotional exhaustion (p > 0.05), depersonalization (p > 0.05), and self-fulfilment (p > 0.05).ConclusionFear of COVID-19 was significantly associated with emotional exhaustion and depersonalization, and inversely associated with self-fulfilment. PNP dentists who exhibited fear of COVID-19 were at greater risk for emotional exhaustion and depersonalization. In developing Burnout syndrome, no significant impact was observed from factors such as age, gender, marital status, children, hierarchy, years of service, work area, private practice, work over 40 h per week, type of service, work performed, sport practice and daily exercise time.

Efficient Classes of Robust Ratio Type Estimators of Mean and Variance in Adaptive Cluster Sampling

This paper proposes two classes of robust ratio type estimators of finite population mean and two classes of robust ratio type estimators of finite population variance using a single auxiliary variable under the adaptive cluster sampling design. Seven robust ratio type estimators have been developed from each class. The generalized expressions of bias and mean square error for each class have been derived up to the first order of approximation. The exact expressions of bias and MSE for all the developed estimators have been presented. Using simulation studies conducted in R programming, the high efficiency of all the developed estimators over similar existing estimators in adaptive cluster sampling has been demonstrated. . KEYWORDS :Adaptive cluster sampling, Classes of robust ratio type estimators, Hidden clustered data, Estimation of mean, Estimation of variance.

The effect of number of clusters and magnitude of within-cluster homogeneity in outcomes on the performance of four variance estimators for a marginal multivariable Cox regression model fit to clustered data in the context of observational research.

Researchers often estimate the association between the hazard of a time-to-event outcome and the characteristics of individuals and the clusters in which individuals are nested. Lin and Wei's robust variance estimator is often used with a Cox regression model fit to clustered data. Recently, alternative variance estimators have been proposed: the Fay-Graubard estimator, the Kauermann-Carroll estimator, and the Mancl-DeRouen estimator. Using Monte Carlo simulations, we found that, when fitting a marginal Cox regression model with both individual-level and cluster-level covariates: (i) in the presence of weak to moderate within-cluster homogeneity of outcomes, the Lin-Wei variance estimator can result in estimates of the SE with moderate bias when the number of clusters is fewer than 20-30, while in the presence of strong within-cluster homogeneity, it can result in biased estimation even when the number of clusters is as large as 100; (ii) when the number of clusters was less than approximately 20, the Fay-Graubard variance estimator tended to result in estimates of SE with the lowest bias; (iii) when the number of clusters exceeded approximately 20, the Mancl-DeRouen estimator tended to result in estimated standard errors with the lowest bias; (iv) the Mancl-DeRouen estimator used with a t-distribution tended to result in 95% confidence that had the best performance of the estimators; (v) when the magnitude of within-cluster homogeneity in outcomes was strong or very strong, all methods resulted in confidence intervals with lower than advertised coverage rates even when the number of clusters was very large.

Enhanced Estimation of Population Variance under Simple Random Sampling with an Application to Real Data

In this paper, we present an enhanced class of estimators for finite population variance under simple random sampling that incorporates an auxiliary information. The equations for bias and mean square error of the proposed estimators are produced up to the first order of approximation. Some well-known estimators are found as a special case of the proposed estimators for suitably chosen values of the scalars. The effectiveness of the proposed estimators is calculated in relation to the existing estimators both theoretically and empirically using five real data sets. The empirical results exhibit the superiority of the suggested estimators over to the existing estimators in each population.. KEYWORDS :Auxiliary variable, Mean square error, Variance estimation, Class of estimators.