Sample Variance Research Articles

Recalibrating single-study effect sizes using hierarchical Bayesian models.

There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance. We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method. The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.

Analysis of the results of preventive testing of bushings using elements of the theory of statistical hypothesis testing

The article presents the results of a study of the dependence of insulation indicators on the duration of operation for serviceable and faulty high-voltage oil-filled bushings, both of hermetic and non-hermetic construction. The results of periodic tests of oil-filled bushings, with a voltage of 110 kV of hermetic design with paper-oil insulation of the capacitor type, as well as non-hermetic construction, were used as initial data. Three statistical criteria were used to form statistically homogeneous data sets: the Wilcoxon rank test, the Z-test, and the Fisher-Snedecor test. The values of the sample mean, sample variance, as well as coefficients of asymmetry and excess of insulation indicators of high-voltage bushings were obtained for the array of initial data and arrays obtained during statistical processing. The values of the sample averages for the arrays obtained as a result of the statistical processing procedure differ significantly. There is a significant decrease in the sample variance values for tgd1 compared to the original arrays. To check the homogeneity of the data in the obtained arrays, the one-factor variance analysis model is used. The practical use of this algorithm made it possible to significantly reduce the heterogeneity of the results of periodic tests of the state of insulation of high-voltage bushings. The analysis of the composition of the arrays with homogeneous values of the indicators made it possible to establish that the values of the indicators of bushings from different regions of Ukraine, of different types and made with different numbers of factory drawings, got into the homogeneous data arrays. It is noted that for high-voltage bushings that do not have insulation defects, the values of the mathematical expectation of the same indicator for different arrays of preventive tests of capacitor-type insulation have a shift relative to each other. From this it follows that the load factor and the operating time of the transformer significantly affect the determination of the maximum allowable value of the insulation index of the high-voltage bushing.

Investigation of parameters for fault detection of worm gear box using denoise vibration signature

In industrial applications, worm gearboxes are a key element. In a worm gearbox, as the material of a worm wheel is softer than that of a worm screw, the worm wheel gear is vulnerable to failure through various modes like pitting, wearing out, or tooth breakage during the sliding process. Due to this, it is essential to monitor the failure of the worm wheel gear of the worm gearbox, and it has gained importance for the diagnosis of faults in gearboxes. The present work focuses on the investigation of the effect of worm wheel tooth breakage, worm wheel bearing outer race, and varying load on vibration signature amplitude and frequency domain statistical features such as root mean square (RMS), crest factor, kurtosis, mean, peak to peak, skewness, sample variance, and standard deviation. The experimental setup is fabricated to conduct the experimental trials. An OR34 FFT analyzer with NVGate software is used to acquire the frequency domain vibration signature. Experimental results show that captured vibration signature amplitude for healthy worm wheel and bearing increased as fault occurred on the worm wheel, and bearing and frequency domain statistical features value changed with the change in fault location in the worm gearbox.

Detecting, mapping and digitising canopy geometry, fruit number and peel colour in pear trees with different architecture

Increasing farm labour and input costs and the requirement for more orchard data are leading to rapid advances in technology to improve management systems in fruit production. The study aimed to (i) validate a sensorised platform to estimate fruit number, peel colour and blush coverage in pear orchards with different pear selections and tree architectures, (ii) establish relationships between fruit number, peel colour and canopy geometry features, and (iii) evaluate the platform for mapping and digitising orchard features. The study was carried out over two years in an experimental ‘ANP-0131′ orchard and in one year in two commercial pear orchards (‘ANP-0131′ and ‘PremP009’). Predictions of fruit number and blush coverage were compared in traditional three-dimensional (3D) and modern high-density two-dimensional (2D) training systems. Overall, prediction errors for fruit number were < 6.5 % in all the training systems, but improved performance was achieved in vertical 2D configurations (% standard errors = 2.2 %). Fruit number and blush coverage per unit of leaf area were higher in 2D compared to 3D training systems. Blush coverage predictions in ‘ANP-0131′ were reliable (R2 = 0.67, RMSE = 3.70 %). Accurate predictions of blush coverage classes were achieved by modifying sample variance. Fruit number and blush coverage were negatively affected by increasing canopy size. The platform proved useful for mapping and digitisation. Spatial heatmaps of orchard features provided a valuable visual aid to identify zones for priority interventions for peel colour enhancement.

Application of Sampling Variance Smoothing Methods for Small Area Proportion Estimation

Abstract Sampling variance smoothing is an important topic in small area estimation. In this article, we propose sampling variance smoothing methods for small area proportion estimation. In particular, we consider the generalized variance function and design effect methods for sampling variance smoothing. We evaluate and compare the smoothed sampling variances and small area estimates based on the smoothed variance estimates through analysis of survey data from Statistics Canada. The results from real data analysis and simulation study indicate that the proposed sampling variance smoothing methods perform very well for small area estimation.

Two-step conditional least squares estimation in ADCINAR(1) process, revisited

Asymptotic normality of two-step conditional least squares estimator is revisited for the stationary ADCINAR(1) process. It turns out that plugging a consistent estimator for the parameter α affects the resulting asymptotic variance, whereas plugging the sample mean and variance has no effect. The phenomenon cannot be grasped from two examples discussed in Karlsen and Tjøstheim (1988).

Bias and Variance in Multiparty Election Polls

Abstract Recent polling failures highlight that election polls are prone to biases that the margin of error customarily reported with polls does not capture. However, such systematic errors are difficult to assess against the background noise of sampling variance. Shirani-Mehr et al. (2018) developed a hierarchical Bayesian model to disentangle random and systematic errors in poll estimates of two-party vote shares at the election level. The method can inform realistic assessments of poll accuracy. We adapt the model to multiparty elections and improve its temporal flexibility. We then estimate bias and variance in 5,240 German national election polls, 1994–2021. Our analysis suggests that the average absolute election-day bias per party was about 1.5 percentage points, ranging from 0.9 for the Greens to 3.2 for the Christian Democrats. The estimated variance is, on average, about twice as large as that implied by usual margins of error. We find little evidence of house or mode effects. Common biases indicate industry effects due to similar methodological problems. The Supplementary Material provides additional results for 1,751 regional election polls.

Robust estimation of mean-variance relation.

Accurate assessment of the mean-variance relation can benefit subsequent analysis in biomedical research. However, in most biomedical data, both the true mean and the true variance are unavailable. Instead, raw data are typically used to allow forming sample mean and sample variance in practice. In addition, different experimental conditions sometimes cause a slightly different mean-variance relation from the majority of the data in the same data set. To address these issues, we propose a semiparametric estimator, where we treat the uncertainty in the sample mean as a measurement error problem, the uncertainty in the sample variance as model error, and use a mixture model to account for different mean-variance relations. Asymptotic normality of the proposed method is established and its finite sample properties are demonstrated by simulation studies. The data application shows that the proposed method produces sensible results compared with methods either ignoring the uncertainty in the sample means or ignoring the potential different mean-variance relations.

Evaluation of sense of safety and privacy in parks in relation to the topography, the presence of dense vegetation and other people in the area

The presence of dense vegetation in a park, its topography and the presence of people are elements of Appleton’s prospect-refuge theory and can affect the level of safety and privacy felt by park visitors in various ways. Here we attempt to identify the nature of the relationship between the height of the observation point in relation to the surroundings (flat, higher or lower ground), the location of dense vegetation (near or far away), the presence (or absence) of other people in the vicinity, and the sense of privacy and safety. We analysed the differences in respondents’ assessments for 12 experimental scenarios taking into account the variability of the above factors. The method was based on factorial designs. The respondents rated the variables from a set of visualisations that differed systematically in terms of the independent variables. The study involved 150 people who evaluated computer visualisations of park landscape in terms of privacy and safety. We compared the respondents’ mean scores for the experimental scenarios by performing three-way analyses of variance for dependent samples. The respondents considered flat or lower ground without the presence of another person in the vicinity to be the safest. In terms of privacy, landscapes higher up, surrounded by dense vegetation and without the presence of other people in the vicinity, were rated the highest. The results yielded some practical conclusions. Privacy without compromising safety will be offered by places located away from people who might attack (safety) or monitor us (privacy). Flat ground seems to be optimal from the point of view of safety and privacy at the same time – this level is the safest, and privacy may be enhanced by nearby dense vegetation.

Measurement plan targeting the accuracy of calibrated chloride ingress model for concrete structures in marine environment

Surface chloride concentration (CS) and chloride diffusion coefficient (DCl) are key parameters for durability assessment of concrete structures in marine environment; they are time-varying and highly dependent on the exposure condition. To reasonably model their behaviors at a specific location, durability measurement data are often needed to calibrate the apparent chloride ingress model based on Fick’s second law. In view of the significant variability of measurements and the bias of chloride ingress model, it remains unaddressed how to formulate a measurement plan to make the calibrated model achieve the required accuracy. This paper first establishes the probabilistic time-dependent models of CS and DCl with both sample variance and model bias considered, and then introduces the Bayesian method to update the two models using measurement data. By assuming realistic models of CS and DCl and comparing them with updated ones, the effectiveness of Bayesian updating method is demonstrated, and the key factors affecting the updated model accuracy are discussed, including prior estimate of parameters, model bias and measuring times. On this basis, a determination method of measurement plan targeting the calibrated model accuracy is proposed, which works for both Bayesian updating and linear fitting for model calibration. And finally numerical examples are presented to show the validity of the proposed method. The sample size obtained by the proposed method is exact for linear fitting and slightly more than required for Bayesian updating.

Informativeness of statistical processing of experimental measurements by the modified Bush-Wind criterion

The use of effective decision-making criteria is very important, especially when it comes to ensuring information security. Controlled attributes, such as keyboard handwriting charac-teristics, intensity of network attacks, and many others, are described by random variables whose distribution laws are usually unknown. Classical nonparametric statistics suggests comparing samples of random variables by rank-based homogeneity criteria that are inde-pendent of the type of distribution. Using the Van der Warden shift criterion and the Klotz scale criterion, Bush and Wind proposed the combined Bush-Wind criterion. It is an asymp-totically optimal nonparametric statistic for equal testing of two normal means and sample variances in a population. The article considers the problem of testing the hypothesis of sta-tistical homogeneity of two experimental measurement samples if the Van der Warden and Klotz criteria, which are formed by approximations of the inverse Gaussian functions, are re-placed by their analogues - the inverse functions of logistic random variables. Computational experiments are carried out and the informativeness of the classical Bush-Wind criterion and its analog, which is formed on the logistic inverse distribution function, is investigated. The analog of the Bush-Wind criterion proposed in this paper differs from the classical criterion by reducing computational complexity while maintaining efficiency. The empirical probabili-ties of recognizing the homogeneity of samples, obtained by conducting computational ex-periments for samples of logistic, Rayleigh and exponential random variables, indicate non-parametricity, high sensitivity and the possibility of applying the criterion in conditions of limited experimental data. The modified Bush-Wind criterion is characterized by high infor-mation content and can be recommended for statistical processing of experimental measure-ments.

The relationship between sleep quality and daytime dysfunction among college students in China during COVID-19: a cross-sectional study.

College Students' sleep quality and daytime dysfunction have become worse since the COVID-19 outbreak, the purpose of this study was to explore the relationship between sleep quality and daytime dysfunction among college students during the COVID-19 (Corona Virus Disease 2019) period. This research adopts the form of cluster random sampling of online questionnaires. From April 5 to 16 in 2022, questionnaires are distributed to college students in various universities in Fujian Province, China and the general information questionnaire and PSQI scale are used for investigation. SPSS26.0 was used to conduct an independent sample t-test and variance analysis on the data, multi-factorial analysis was performed using logistic regression analysis. The main outcome variables are the score of subjective sleep quality and daytime dysfunction. During the COVID-19 period, the average PSQI score of the tested college students was 6.17 ± 3.263, and the sleep disorder rate was 29.6%, the daytime dysfunction rate was 85%. Being female, study liberal art/science/ engineering, irritable (due to limited outdoor), prolong electronic entertainment time were associated with low sleep quality (p < 0.001), and the occurrence of daytime dysfunction was higher than other groups (p < 0.001). Logistics regression analysis showed that sleep quality and daytime dysfunction were associated with gender, profession, irritable (due to limited outdoor), and prolonged electronic entertainment time (p < 0.001). During the COVID-19 epidemic, the sleep quality of college students was affected, and different degrees of daytime dysfunction have appeared, both are in worse condition than before the COVID-19 outbreak. Sleep quality may was inversely associated with daytime dysfunction.

Experiment-based calibration in psychology: Optimal design considerations

Psychological theories are often formulated at the level of latent, not directly observable, variables. Empirical measurement of latent variables ought to be valid. Classical psychometric validity indices can be difficult to apply in experimental contexts. A complementary validity index, termed retrodictive validity, is the correlation of theory-derived predicted scores with actually measured scores, in specifically designed calibration experiments. In the current note, I analyse how calibration experiments can be designed to maximise the information garnered and specifically, how to minimise the sample variance of retrodictive validity estimators. First, I harness asymptotic limits to analytically derive different distribution features that impact on estimator variance. Then, I numerically simulate various distributions with combinations of feature values. This allows deriving recommendations for the distribution of predicted values, and for resource investment, in calibration experiments. Finally, I highlight cases in which a misspecified theory is particularly problematic.

153 Genomic Prediction and Genetic Parameters of Residual Feed Intake Computed Using Linear and Non-Linear Regression Methods in Broiler Chickens

Abstract Feed efficiency traits are important for reducing feeding costs while potentially decreasing the environmental impact of broiler production. This study aimed to compare the predictive ability of different genome-wide prediction models for residual feed intake (RFI) calculated with linear and non-linear regression techniques. We also evaluated the heritability of such RFI as well as their associations with body weight. Genotypes and phenotypes used in this study were recorded from 32,747 purebred broiler chickens of both sexes. The individual feed intake was monitored daily in floor-raised birds using a radio-frequency antenna system. RFI was computed as the residual term of total feed intake (FI) adjusted for the average metabolic weight (AMW), body weight gain (BWG), and sex. The following regression methods were used to compute RFI: multiple linear regression (MLR), generalized additive model using splines (GAM), support vector regression using a radial basis kernel (SVR), and two-hidden layer neural network (NN). The different RFI assessments were fitted jointly with body weight measured at the end of the feeding trial (FBW) using a two-trait mixed model. The effects of contemporary groups were fitted as fixed for both traits, whereas sex was also considered for FBW. RFI (computed with different regression techniques) was predicted using different genomic prediction models (BayesA, BayesB, BayesCπ, GBLUP, RKHS, and SVR). Genomic prediction accuracy (ACC) was assessed using a random forward validation scheme, in which data from previous generations were used for training the models while later generations were used to test predictions. The effect of the RFI computation method on ACC was tested using an unpaired Wilcoxon test. Sample variance observed for RFI ranged between 57,207.04 and 59,254.76, depending on the regression method used to compute it. Overall, the RFI sample variance was smaller for non-linear methods (NN, SVR, and GAM) compared with the linear model (MLR). The regression methods did not impact the genetic parameter estimates for RFI, which presented a heritability value around 0.32 ± 0.015 and genetic correlation with FBW ranging between 0.22 ± 0.04 and 0.25 ± 0.04. Prediction accuracy across genomic regression methods was slightly higher (P-value = 0.038) when RFI was computed with SVR (0.336 ± 0.023) compared with the MLR (0.329 ± 0.025), although this small difference would have little practical implications. No differences in terms of ACC were observed among the other regression methods used to compute RFI. Our preliminary results indicate that the regression technique used to compute RFI does not have major implications for this trait. Therefore, the MLR method is the preferred choice as it is simpler to implement, and it provides easy-to-interpret parameters.

Variability of temperature measurements recorded by a wearable device by biological sex

BackgroundFemales have been historically excluded from biomedical research due in part to the documented presumption that results with male subjects will generalize effectively to females. This has been justified in part by the assumption that ovarian rhythms will increase the overall variance of pooled random samples. But not all variance in samples is random. Human biometrics are continuously changing in response to stimuli and biological rhythms; single measurements taken sporadically do not easily support exploration of variance across time scales. Recently we reported that in mice, core body temperature measured longitudinally shows higher variance in males than cycling females, both within and across individuals at multiple time scales.MethodsHere, we explore longitudinal human distal body temperature, measured by a wearable sensor device (Oura Ring), for 6 months in females and males ranging in age from 20 to 79 years. In this study, we did not limit the comparisons to female versus male, but instead we developed a method for categorizing individuals as cyclic or acyclic depending on the presence of a roughly monthly pattern to their nightly temperature. We then compared structure and variance across time scales using multiple standard instruments.ResultsSex differences exist as expected, but across multiple statistical comparisons and timescales, there was no one group that consistently exceeded the others in variance. When variability was assessed across time, females, whether or not their temperature contained monthly cycles, did not significantly differ from males both on daily and monthly time scales.ConclusionsThese findings contradict the viewpoint that human females are too variable across menstrual cycles to include in biomedical research. Longitudinal temperature of females does not accumulate greater measurement error over time than do males and the majority of unexplained variance is within sex category, not between them.

The two-loop power spectrum in redshift space

We present the matter power spectrum in redshift space including two-loop corrections. We follow a strictly perturbative approach incorporating all non-linearities entering both via the redshift-space mapping and within real space up to the required (fifth) order, complemented by suitable effective field theory (EFT) corrections. This approach can a priori be viable up to scales of order 0.2h Mpc-1 beyond which power suppression related to the finger-of-God effect becomes non-perturbatively strong. We extend a simplified treatment of EFT corrections at two-loop order from real to redshift space, making sure that the leading UV-sensitivity of both the single-hard and double-hard limit of the two-loop contributions to the power spectrum is accounted for, and featuring two free parameters for each multipole. Taking also infrared-resummation into account, we calibrate with and compare to Quijote N-body simulations for the monopole and quadrupole at redshifts z = 0 and z = 0.5. We find agreement within sample variance (at percent-level) up to 0.18h Mpc-1 at two-loop order, compared to 0.1h Mpc-1 at one-loop. We also investigate the role of higher-derivative corrections.

Extreme data compression for Bayesian model comparison

We develop extreme data compression for use in Bayesian model comparison via the MOPED algorithm, as well as more general score compression. We find that Bayes Factors from data compressed with the MOPED algorithm are identical to those from their uncompressed datasets when the models are linear and the errors Gaussian. In other nonlinear cases, whether nested or not, we find negligible differences in the Bayes Factors, and show this explicitly for the Pantheon-SH0ES supernova dataset. We also investigate the sampling properties of the Bayesian Evidence as a frequentist statistic, and find that extreme data compression reduces the sampling variance of the Evidence, but has no impact on the sampling distribution of Bayes Factors. Since model comparison can be a very computationally-intensive task, MOPED extreme data compression may present significant advantages in computational time.

A generalized class of estimators for the mean using multiauxiliary information in adaptive cluster sampling

This article addresses the problem of estimating the population mean of variable y using information from multiple auxiliary variables in adaptive cluster sampling. We define a class of estimators based on p(>1) auxiliary variables x 1 , x 2 , … . . , x p , derive bias and mean squared error expressions up to order n−1, and determine optimal conditions for minimizing mean squared error. Our findings demonstrate that the proposed estimators outperform the multiple linear regression estimator. We also identify several existing estimators as members of this class, including those introduced by Dryver and Chao (2007), Chutiman and Kumphon (2008), Chutiman (2013), Chaudhry (2014), Chaudhry and Hanif (2015), and Yadav et al. (2016). Furthermore, we obtain the correct values of constants “a” and “b” for the Chaudhry and Hanif (2015) estimator, leading to an accurate expression for its minimum mean squared error. To assess the performance of these estimators, we conduct a simulation study.

A Bayesian Semi-parametric Modelling Approach for Area Level Small Area Studies

We present a new semiparametric extension of the Fay-Herriot model, termed the agnostic Fay-Herriot model (AGFH), in which the sampling-level model is expressed in terms of an unknown general function [Formula: see text]. Thus, the AGFH model can express any distribution in the sampling model since the choice of [Formula: see text] is extremely broad. We propose a Bayesian modelling scheme for AGFH where the unknown function [Formula: see text] is assigned a Gaussian Process prior. Using a Metropolis within Gibbs sampling Markov Chain Monte Carlo scheme, we study the performance of the AGFH model, along with that of a hierarchical Bayesian extension of the Fay-Herriot model. Our analysis shows that the AGFH is an excellent modelling alternative when the sampling distribution is non-Normal, especially in the case where the sampling distribution is bounded. It is also the best choice when the sampling variance is high. However, the hierarchical Bayesian framework and the traditional empirical Bayesian framework can be good modelling alternatives when the signal-to-noise ratio is high, and there are computational constraints. AMS subject classification: 62D05; 62F15

CHANGES IN CYTOKINE BALANCE IN PREGNANT WOMEN WITH CHRONIC ENDOMETRITIS IN THE PAST MEDICAL HISTORY AND THEIR ROLE IN THE DEVELOPMENT OF PREECLAMPSIA

CHANGES IN CYTOKINE BALANCE IN PREGNANT WOMEN WITH CHRONIC ENDOMETRITIS IN THE PAST MEDICAL HISTORY AND THEIR ROLE IN THE DEVELOPMENT OF PREECLAMPSIA