Variance Estimation Method Research Articles

Meta-analysis with Robust Variance Estimation: Expanding the Range of Working Models.

In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the "metafor" and "clubSandwich" packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.

Spatial Multiresolution Analysis Approach to Identify Crash Hotspots and Estimate Crash Risk

AbstractIn this paper, we evaluate the performance of a spatial multiresolution analysis (SMA) method, that behaves like a variable bandwidth kernel density estimation (KDE) method, for hazardous r...

Estimation of Queue Length at Signalized Intersection : A Review

In this growing world, traffic conjunction is considered as most growing problems in both urban as well as sub-urban areas. Traffic congestion is a major issue in day to day life. It has a tremendous impact on the nation’s economy, air pollution, water pollution, travel behavior, accidental risk, land-use and to its end users. Various studies have shown that there is a sudden increase in the traffic congestion. Traffic congestion leads to queuing. Queuing process takes place in both signalized as well as non-signalized intersections, freeways, bottlenecks, incident sites, toll plazas, parking and the areas near the freeways. Queuing also occurs in many non transportation fields. Hence it is very important to study the queue length pattern, its estimation, methods of calculating queue length and give a solution. Hence various methods for estimation of queue length is reviewed.

Compliance of the cerebrospinal space: comparison of three methods

BackgroundCerebrospinal compliance describes the ability of the cerebrospinal space to buffer changes in volume. Diminished compliance is associated with increased risk of potentially threatening increases in intracranial pressure (ICP) when changes in cerebrospinal volume occur. However, despite various methods of estimation proposed so far, compliance is seldom used in clinical practice. This study aimed to compare three measures of cerebrospinal compliance.MethodsICP recordings from 36 normal-pressure hydrocephalus patients who underwent infusion tests with parallel recording of transcranial Doppler blood flow velocity were retrospectively analysed. Three methods were used to calculate compliance estimates during changes in the mean ICP induced by infusion of fluid into the cerebrospinal fluid space: (a) based on Marmarou’s model of cerebrospinal fluid dynamics (CCSF), (b) based on the evaluation of changes in cerebral arterial blood volume (CCaBV), and (c) based on the amplitudes of peaks P1 and P2 of ICP pulse waveform (CP1/P2).ResultsIncrease in ICP caused a significant decrease in all compliance estimates (p < 0.0001). Time courses of compliance estimators were strongly positively correlated with each other (group-averaged Spearman correlation coefficients: 0.94 [0.88–0.97] for CCSF vs. CCaBV, 0.77 [0.63–0.91] for CCSF vs. CP1/P2, and 0.68 [0.48–0.91] for CCaBV vs. CP1/P2).ConclusionsIndirect methods, CCaBV and CP1/P2, allow for the assessment of relative changes in cerebrospinal compliance and produce results exhibiting good correlation with the direct method of volumetric manipulation. This opens the possibility of monitoring relative changes in compliance continuously.

Semiparametric Bayes instrumental variable estimation with many weak instruments

We develop a new semiparametric Bayes instrumental variables estimation method. We employ the form of the regression function of the first‐stage equation, and the disturbances are modeled nonparametrically to achieve better predictive power of the endogenous variables, whereas we use parametric formulation in the second‐stage equation, which is of interest in inference. Our simulation studies show that under small sample sizes, the proposed method obtains more efficient estimates and very precise credible intervals compared with existing IV methods with smaller mean squared error. We applied our proposed method to a Mendelian randomization dataset where a large number of instruments are available and semiparametric specification is appropriate. We obtained statistically significant results that cannot be obtained by the existing methods, including standard Bayesian IV.

Minimum Average Variance Estimation with group Lasso for the multivariate response Central Mean Subspace

The Minimum Average Variance Estimation (MAVE) method and its variants have proven to be effective approaches to the dimension reduction problems. However, as far as we know, using MAVE to estimate the Central Mean Subspace (CMS) with multivariate response receives little attention. This paper proposes a weighted version of MAVE for the CMS with multivariate response. The proposed weighted MAVE method takes account of the correlations among the responses and is a natural extension of the original MAVE method that only targets the univariate response CMS. The algorithm to implement the weighted MAVE method is provided. Asymptotic distribution of the MAVE estimator under the multivariate response setting is also derived. In addition, for the goal of variable selection, we propose incorporating the adaptive group lasso regularization method to induce sparse solutions. We show that the resulting sparse estimator can be calculated in a computationally efficient manner, and the associated BIC-type criterion can consistently select relevant variables. Experimental simulations and a real data analysis demonstrate the effectiveness and usefulness of the proposed methods.

Terrorism, innovation and venture capital

PurposeThis paper aims to examine the relationship between terrorism and innovation and the moderating role of venture capital.Design/methodology/approachThe paper has used panel data from 140 countries covering the period of 2007–2016 and has analysed the data by using generalised method of moments instrumental variables (GMM-IV) estimation method to control for unobserved endogeneity among the variables.FindingsThe authors find that terrorism has negative impact on innovation. Interesting results emerge when we separated the developed countries from others. The results show that the impact of terrorism on innovation is lower in developed countries. This is due to the fact that strong institutional settings in developed countries make the investors confident by providing support and incentives. Better institutional settings in developed countries also help to reduce uncertainty, which maximise innovation and minimise terrorism risk. The authors also find that venture capital positively moderates the terrorism and innovation relationship. This implies that by providing sufficient fund for technological development, venture capital may help to reduce terrorism risk.Practical implicationsThese results may guide the policy makers to find a business solution instead of lengthy political solution to mitigate terrorism risk in emerging countries. Overall, this paper will provide the basis for improving the counter-terrorism approaches from an innovation perspective.Originality/valueThe paper has used terrorism and venture capital data from 140 countries and finds interesting results that may help the policy makers to reduce the effect and intensity of terrorism in emerging countries.

Parametric Versus Semi and Nonparametric Regression Models

There are three common types of regression models: parametric, semiparametric and nonparametric regression. The model should be used to fit the real data depends on how much information is available about the form of the relationship between the response variable and explanatory variables, and the random error distribution that is assumed. Researchers need to be familiar with each modeling approach requirements. In this paper, differences between these models, common estimation methods, robust estimation, and applications are introduced. For parametric models, there are many known methods of estimation, such as least squares and maximum likelihood methods which are extensively studied but they require strong assumptions. On the other hand, nonparametric regression models are free of assumptions regarding the form of the response-explanatory variables relationships but estimation methods, such as kernel and spline smoothing are computationally expensive and smoothing parameters need to be obtained. For kernel smoothing there two common estimators: local constant and local linear smoothing methods. In terms of bias, especially at the boundaries of the data range, local linear is better than local constant estimator.&amp;nbsp; Robust estimation methods for linear models are well studied, however the robust estimation methods in nonparametric regression methods are limited. A robust estimation method for the semiparametric and nonparametric regression models is introduced.

Rapid Blind Denoising Method for Grating Fringe Images Based on Noise Level Estimation

Object profile measurement theory and technology based on grating fringe image projection are popular topics of research. However, due to the presence of noise, the phase extracted from captured grating fringe images may be wrong, affecting the measurement results. To manage noise with unknown intensity levels, we use Gaussian distribution to model the noise, and propose a novel rapid blind denoising method. First, we establish an energy expression model for grating fringe images. After performing singular value decomposition (SVD) on the grating fringe image, we establish a linear relationship between the average energy of the image and the noise variance. Second, we propose a noise variance estimation method based on a single frame grating fringe image. By adding an auxiliary image of zero-mean Gaussian noise, we can obtain the estimated noise variance. Third, a Gaussian filtering method of the phase maps using the estimated variance, is employed to reduces the amount of data processing. The experimental results show that the proposed method can complete noise processing faster than five other methods, and extract the phase profile of two measured models with minimum root-mean-square (RMS) error of 0.0686 and 0.0673 rad.

Probabilistic Load Flow Calculation of Power System Integrated with Wind Farm Based on Kriging Model

Because of the randomness and uncertainty, integration of large-scale wind farms in a power system will exert significant influences on the distribution of power flow. This paper uses polynomial normal transformation method to deal with non-normal random variable correlation, and solves probabilistic load flow based on Kriging method. This method is a kind of smallest unbiased variance estimation method which estimates unknown information via employing a point within the confidence scope of weighted linear combination. Compared with traditional approaches which need a greater number of calculation times, long simulation time, and large memory space, Kriging method can rapidly estimate node state variables and branch current power distribution situation. As one of the generator nodes in the western Yunnan power grid, a certain wind farm is chosen for empirical analysis. Results are used to compare with those by Monte Carlo-based accurate solution, which proves the validity and veracity of the model in wind farm power modeling as output of the actual turbine through PSD-BPA.

The Mediating Role of Labour Supply in the Relationship between Wage and Fertility for Ghanaian Women. A Marginal Mediation Analysis Approach.

This study explored the effect of wage on fertility rate for Ghanaian women with labour supply as a mediating variable. Using data from GLSS 6, it employed the Marginal Mediation Analysis, which combined average marginal effect and appropriate estimation techniques, to decompose the total effect into direct and indirect effects. Endogeneity was inherent in the mediation model and therefore respondents’ belongingness to a trade union was used as an instrument for wage to correct for the anomaly. Two different instrumental variable estimation methods, namely the two stage least squares (2SLS) and the two stage residual inclusion (2SRI), were used after which the average marginal effects of the estimated coefficients were computed. The 2SLS was appropriate for the equation which had labour supply (hours of work) as its dependent variable because the regressand is a continuous variable. In contrast, the equations whose dependent variable was number of children were estimated by the 2SRI due to the nonlinearity of their measured variable. It was revealed that a percentage increase in wage directly reduces the number of children per woman by 0.85. Also, a percentage rise in wage decreases the number of children per woman by 0.016 through labour supply. Thus empowering women in terms of both their earnings and job opportunities on the labour market is effective in combating high fertility rate among Ghanaian women. The more lucrative the job market is, the greater the opportunity cost of home production activities and hence fertility rate will drop.

Various Methods for Estimation of Biomechanical Properties of Bone to Determine Strength of Bone

Various Methods for Estimation of Biomechanical Properties of Bone to Determine Strength of Bone

Estimating the Population Attributable Fraction of Asthma Due to Electronic Cigarette Use and Other Risk Factors Using Kentucky Behavioral Risk Factor Survey Data, 2016–2017

Introduction: Electronic nicotine delivery systems ENDS have become popular in the United States among both new users of nicotine and those seeking less harmful alternatives to traditional cigarettes. Users often perceive ENDS as being less harmful than traditional cigarettes. This study investigated the relationship between use of ENDS and asthma in a representative sample of adults. Methods: For this cross-sectional study, we used data from the Kentucky Behavioral Risk Factor Surveillance System telephone survey data from 2016-2017. Using a weighted multivariable logistic regression analysis, we identified important covariates to adjust for to calculate the population attributable fraction (PAF) of asthma due to ENDS and other modifiable risk factors factors (cigarette use, obesity, education, and employment). The confidence intervals for the PAFs were estimated using bootstrap methods of variance estimation. Results: We found that 10.6% of those aged 18-30 reported currently had asthma. After adjusting for noted covariates, ENDS use did not significantly increase the odds of asthma. In the final PAF model, the PAF of asthma due to ENDS was 0.4% (95% CI: -5.41, 6.21). Conclusion: While these findings suggest only modest effects of ENDS use on asthma prevalence, future research including older age groups and more long-term users might produce different results.

Aggregation of data from 4 clinical studies demonstrating efficacy of single-dose doxycycline postexposure for prevention of the spirochetal infections: Lyme disease, syphilis, and tick-borne relapsing fever

Single-dose doxycycline, given within 72 hours of exposure, has been evaluated in 4 clinical studies for postexposure prophylaxis of 3 spirochetal infections: Lyme disease, syphilis, and tick-borne relapsing fever. In this study, data from the 4 studies were aggregated using a meta-analytic random-effects approach, with the DerSimonian-Laird method for variance estimation, analyzing a total of 2468 subjects. The findings of this analysis demonstrated an overall efficacy rate for prevention of the 3 spirochetal infections of 78% (95% confidence interval: 50%–91%).

On the Estimation of Variance of Calibration Regression Estimators with Multiple Auxiliary Information

This paper introduces the concept of calibration estimators to Statistical Regression Estimation and proposes a multivariate calibration regression (M-REG) estimator of population mean in stratified random sampling. It develops a new approach to variance estimation that is more efficient in estimating populations with multiple auxiliary variables using the principle of analysis of variance (ANOVA). The relative performance of the new variance estimation method with respect to the estimation of variance of the proposed M-REG estimator is compared empirically with a corresponding global variance estimation method. Analysis and evaluation presented, proved the dominance of the suggested new approach to variance estimation.

Iterative GMM for partially linear single-index models with partly endogenous regressors

In this paper, we consider the estimation method for the partially linear single-index model with endogenous regressors in the linear part. The Generalized Method of Moments (GMM) using instrumental variables is applied to cope with the problem that the parameter estimators may be inconsistent due to endogeneity. The GMM estimation is based on an iterative procedure, which has generalized the well known Minimum Average conditional Variance Estimation (MAVE) method, in the sense that in each iteration the estimates of the nonparametric components and the parameter vectors are obtained from the generalized moments equation instead of the least squares optimization. A specific algorithm to implement the estimation procedure concerning the choice of the instruments is provided. Asymptotic properties of the estimators are also established. Simulated experiments show that the proposed estimation method performs well in finite samples. Application to the National Longitudinal Survey of Young Men data illustrates the proposed model and method in analyzing the returns to schooling.

The effect of renewable and non-renewable energy consumption on economic growth: Non-parametric evidence

We employ a non-parametric modelling technique to examine the time-varying impact of renewable and non-renewable energy consumption on economic growth. The specific nonparametric method that we employ is the local linear dummy variable estimation (LLDVE) method, which we apply to OECD and non-OECD panels for the period 1990 to 2015. While previous studies employing parametric models have recognised the existence of non-linearities and instability in the relationship between renewable (and non-renewable) energy consumption and economic growth, the LLDVE method has the advantage that it does not make any assumptions about functional form and, hence, is better able to approximate the non-linear relationship. Our estimates suggest that non-renewable energy consumption exerts a positive and significant impact on economic growth across OECD nations with the coefficient function exhibiting an upward trajectory over time. The impact of renewable energy consumption on economic growth, however, is statistically indistinguishable from zero in these countries for most of the study period. Both renewable and non-renewable energy consumption promote economic growth in non-OECD countries, suggesting that developing countries may play an important role in the transition process to renewables despite constraints in technical progress.

A Meta-Analysis of Single-Case Research on Word-Problem Instruction for Students With Learning Disabilities

In this meta-analysis of single-case designs, the researchers investigated the effects of word-problem instruction for students with learning disabilities in Grades 1 through 12, as reported in 20 peer-reviewed articles published between 1975 and 2020. A two-level multilevel model with a robust variance estimation method was implemented to calculate the effect sizes. A significant overall effect of word-problem instruction for students with learning disabilities was found, and the results indicate that studies meeting all three quality indicators of implementation fidelity (i.e., adherence, dosage, and regularity) showed greater effects. The average weighted effect sizes of word-problem instruction varied depending on the content and practice standards of the Common Core State Standards for Mathematics. Limitations and future research directions are also discussed.

An assessment of statistical methods for nonindependent data in ecological meta-analyses.

In ecological meta-analyses, nonindependence among observed effect sizes from the same source paper is common. If not accounted for, nonindependence can seriously undermine inferences. We compared the performance of four meta-analysis methods that attempt to address such nonindependence and the standard random-effect model that ignores nonindependence. We simulated data with various types of within-paper nonindependence, and assessed the standard deviation of the estimated mean effect size and Type I error rate of each method. Although all four methods performed substantially better than the standard random-effects model that assumes independence, there were differences in performance among the methods. A two-step method that first summarizes the multiple observed effect sizes per paper using a weighted mean and then analyzes the reduced data in a standard random-effects model, and a robust variance estimation method performed consistently well. A hierarchical model with both random paper and study effects gave precise estimates but had a higher Type I error rates, possibly reflecting limitations of currently available meta-analysis software. Overall, we advocate the use of the two-step method with a weighted paper mean and the robust variance estimation method as reliable ways to handle within-paper nonindependence in ecological meta-analyses.

Estimation in Partial Functional Linear Spatial Autoregressive Model

Functional regression allows for a scalar response to be dependent on a functional predictor; however, not much work has been done when response variables are dependence spatial variables. In this paper, we introduce a new partial functional linear spatial autoregressive model which explores the relationship between a scalar dependence spatial response variable and explanatory variables containing both multiple real-valued scalar variables and a function-valued random variable. By means of functional principal components analysis and the instrumental variable estimation method, we obtain the estimators of the parametric component and slope function of the model. Under some regularity conditions, we establish the asymptotic normality for the parametric component and the convergence rate for slope function. At last, we illustrate the finite sample performance of our proposed methods with some simulation studies.