Sample Variance Research Articles

BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification

Data augmentation is a crucial regularization technique for deep neural networks, particularly in medical imaging tasks with limited data. Deep learning models are highly effective at linearizing features, enabling the alteration of feature semantics through the shifting of latent space representations—an approach known as semantic data augmentation (SDA). The paradigm of SDA involves shifting features in a specified direction. Current SDA methods typically sample the amount of shifting from a Gaussian distribution or the sample variance. However, excessive shifting can lead to changes in data labels, which may negatively impact model performance. To address this issue, we propose a computationally efficient method called Bayesian Random Semantic Data Augmentation (BSDA). BSDA can be seamlessly integrated as a plug-and-play component into any neural network. Our experiments demonstrate that BSDA outperforms competitive methods and is suitable for both 2D and 3D medical image datasets, as well as most medical imaging modalities. Additionally, BSDA is compatible with mainstream neural network models and enhances baseline performance. The code is available online.

A GENERAL LIMIT THEORY FOR NONLINEAR FUNCTIONALS OF NONSTATIONARY TIME SERIES

New limit theory is provided for a wide class of sample variance and covariance functionals involving both nonstationary and stationary time series. Sample functionals of this type commonly appear in regression applications and the asymptotics are particularly relevant to estimation and inference in nonlinear nonstationary regressions that involve unit root, local unit root, or fractional processes. The limit theory is unusually general in that it covers both parametric and nonparametric regressions. Self-normalized versions of these statistics are considered that are useful in inference. Numerical evidence reveals interesting strong bimodality in the finite sample distributions of conventional self-normalized statistics similar to the bimodality that can arise in t-ratio statistics based on heavy tailed data. Bimodal behavior in these statistics is due to the presence of long memory innovations and is shown to persist for very large sample sizes even though the limit theory is Gaussian when the long memory innovations are stationary. Bimodality is shown to occur even in the limit theory when the long memory innovations are nonstationary. To address these complications, new self-normalized versions of the test statistics are introduced that deliver improved approximations that can be used for inference.

Depressive and anxiety symptoms in current, previous, and no history of ME/CFS: NHIS 2022 analysis.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is associated with anxiety and depressive symptoms. Psychological symptoms are predisposing factors for, as well as symptoms of, ME/CFS. Recovery from ME/CFS is poorly understood and heterogenous, and it is unclear how psychological symptoms may change with recovery. The aim of this study was to examine the associations of depressive and anxious symptoms among individuals with current, previous, and no history of ME/CFS. National Health Interview Survey 2022 data were analyzed to assess ME/CFS status, as well as anxiety and depression burden. Adults (unweighted N = 27,651) in the United States reported sociodemographic and health behavior characteristics, with 453 adults reporting current ME/CFS, while 119 reported previous ME/CFS. Sample weights and variance estimation variables were implemented. Multivariable linear regression models were used to analyze the associations between ME/CFS status and anxiety and depression severity after adjusting for sociodemographic and health behavior variables. Participants were on average 48.1 years of age, and most identified as female (51.3%), white (76.6%), and not Hispanic or Latine (82.8%). Current and previous ME/CFS were associated with anxiety and depressive symptoms compared to individuals with no history of ME/CFS. Clinically significant levels of anxiety and depressive symptoms were substantial for individuals with current (37.6%; 49.0%) and previous (26.5%; 33.4%) ME/CFS compared to individuals with no history of ME/CFS (6.1%; 6.7%). ME/CFS, regardless of current presence, was related to significantly greater anxiety and depressive symptom burden.

ATBHC-YOLO: aggregate transformer and bidirectional hybrid convolution for small object detection

Object detection using UAV images is a current research focus in the field of computer vision, with frequent advancements in recent years. However, many methods are ineffective for challenging UAV images that feature uneven object scales, sparse spatial distribution, and dense occlusions. We propose a new algorithm for detecting small objects in UAV images, called ATBHC-YOLO. Firstly, the MS-CET module has been introduced to enhance the model’s focus on global sparse features in the spatial distribution of small objects. Secondly, the BHC-FB module is proposed to address the large-scale variance of small objects and enhance the perception of local features. Finally, a more appropriate loss function, WIoU, is used to penalise the quality variance of small object samples and further enhance the model’s detection accuracy. Comparison experiments on the DIOR and VEDAI datasets validate the effectiveness and robustness of the improved method. By conducting experiments on the publicly available UAV benchmark dataset Visdrone, ATBHC-YOLO outperforms the state-of-the-art method(YOLOv7) by 3.5%.

Detection Probabilities of Flathead Catfish in Small Kansas Impoundments

Abstract A primary challenge of Flathead Catfish Pylodictis olivaris management is uncertainty associated with sampling strategies and resulting ambiguity in population-level information. Assessment of impoundment and environmental conditions that affect detection probability may aid in reducing sample variance and benefit inferences regarding changes to Flathead Catfish populations. We sampled eight small impoundments in Kansas (37 – 114 surface ha) using low frequency electrofishing in summer, 2021. We revisited sites nine times over three months using an occupancy modeling framework to estimate the influence of impoundment and environmental conditions on detection probability of Flathead Catfish. We employed an information theoretic approach and ranked models built with impoundment as a random effect and three environmental variables predicted to influence detection of Flathead Catfish in small impoundments. Detection probability across all populations was 0.526 (SE = 0.020) and was influenced by water temperature, mean depth of the impoundment, and proportion of impoundment sampled. Generally, detection probability increased with all measured variables. The inclusion of detection probability in assessments of Flathead Catfish in small impoundments can inform interpretation of catch-related metrics. Further, variable detection suggests collection of multiple samples during a defined sampling period might be more suitable for characterizing populations than a single sample.

MIGHTEE: The continuum survey data release 1

Abstract The MeerKAT International GHz Tiered Extragalactic Exploration Survey (MIGHTEE) is one of the large survey projects using the MeerKAT telescope, covering four fields that have a wealth of ancillary data available. We present Data Release 1 of the MIGHTEE continuum survey, releasing total intensity images and catalogues over ∼20 deg2, across three fields at ∼1.2-1.3 GHz. This includes 4.2 deg2 over the Cosmic Evolution Survey (COSMOS) field, 14.4 deg2 over the XMM Large-Scale Structure (XMM-LSS) field and deeper imaging over 1.5 deg2 of the Extended Chandra Deep Field South (CDFS). We release images at both a lower resolution (7–9 arcsec) and higher resolution (∼5 arcsec). These images have central rms sensitivities of ∼1.3 −2.7 μJy beam−1 (∼1.2 −3.6 μJy beam−1) in the lower (higher) resolution images respectively. We also release catalogues comprised of ∼144 000 (∼114 000) sources using the lower (higher) resolution images. We compare the astrometry and flux-density calibration with the Early Science data in the COSMOS and XMM-LSS fields and previous radio observations in the CDFS field, finding broad agreement. Furthermore, we extend the source counts at the ∼10 μJy level to these larger areas (∼20 deg2) and, using the areal coverage of MIGHTEE we measure the sample variance for differing areas of sky. We find a typical sample variance of 10-20percnt for 0.3 and 0.5 sq. deg. sub-regions at S1.4 ≤ 200 μJy, which increases at brighter flux densities, given the lower source density and expected higher galaxy bias for these sources.

A comparative study on sequential detection of random mean change in multivariate normal data stream

For the detection of random mean changes in a multivariate normal data stream, we explore and compare various direction-invariant monitoring charts. These include the (adaptive) largest eigenvalue charts employing exponential weighted moving variance (EWMV), the moving largest eigenvalue (MLE) based on moving sample variance, and both adaptive and windowed cumulative sum (CUSUM) charts with a reference value for the largest eigenvalue. The comparison involves assessing the power of detection (POD) given false detection probability (FDP) at a signal length and the average delay detection time (ADDT) given a false alarm rate (FAR) at a change time. Our primary contribution involves introducing the moving generalized likelihood ratio (MGLR) chart, which relies solely on the chi-square property of the adjusted generalized likelihood ratio. We provide simple approximations for FDP and FAR for design purposes. Our results indicate that the EWMV chart is recommended, and the MGLR chart performs effectively. Detection of volatility change in 30 Dow Jones Industrial stock prices is used for illustration.

Path integral molecular dynamics approximations of quantum canonical observables

Mean-field molecular dynamics based on path integrals is used to approximate canonical quantum observables for particle systems consisting of nuclei and electrons. A computational bottleneck is the Monte Carlo sampling from the Gibbs density of the electron operator, which due to the fermion sign problem has a computational complexity that scales exponentially with the number of electrons. In this work, we construct an algorithm that approximates the mean-field Hamiltonian by path integrals for fermions. The algorithm is based on the determinant of a matrix with components based on Brownian bridges connecting permuted electron coordinates. The computational work for n electrons is O(n3), which reduces the computational complexity associated with the fermion sign problem. We analyze a bias resulting from this approximation and provide a rough computational error indicator. It remains to rigorously explain the surprisingly high accuracy for high temperatures. The method becomes infeasible at low temperatures due to a large sample variance.

The Meaning and Measure of Concordance Factors in Phylogenomics.

As phylogenomic datasets have grown in size, researchers have developed new ways to measure biological variation and to assess statistical support for specific branches. Larger datasets have more sites and loci and therefore less sampling variance. While we can more accurately measure the mean signal in these datasets, lower sampling variance is often reflected in uniformly high measures of branch support-such as the bootstrap and posterior probability-limiting their utility. Larger datasets have also revealed substantial biological variation in the topologies found across individual loci, such that the single species tree inferred by most phylogenetic methods represents a limited summary of the data for many purposes. In contrast to measures of statistical support, the degree of underlying topological variation among loci should be approximately constant regardless of the size of the dataset. "Concordance factors" (CFs) and similar statistics have therefore become increasingly important tools in phylogenetics. In this review, we explain why CFs should be thought of as descriptors of topological variation rather than as measures of statistical support, and argue that they provide important information about the predictive power of the species tree not contained in measures of support. We review a growing suite of statistics for measuring concordance, compare them in a common framework that reveals their interrelationships, and demonstrate how to calculate them using an example from birds. We also discuss how measures of topological variation might change in the future as we move beyond estimating a single "tree of life" toward estimating the myriad evolutionary histories underlying genomic variation.

Doubly-robust and heteroscedasticity-aware sample trimming for causal inference

SUMMARY A popular method for variance reduction in causal inference is propensity-based trimming, the practice of removing units with extreme propensities from the sample. This practice has theoretical grounding when the data are homoscedastic and the propensity model is parametric (Yang & Ding, 2018; Crump et al., 2009), but in modern settings where heteroscedastic data are analyzed with non-parametric models, existing theory fails to support current practice. In this work, we address this challenge by developing new methods and theory for sample trimming. Our contributions are three-fold: first, we describe novel procedures for selecting which units to trim. Our procedures differ from previous works in that we trim not only units with small propensities, but also units with extreme conditional variances. Second, we give new theoretical guarantees for inference after trimming. In particular, we show how to perform inference on the trimmed sub-population without requiring that our regressions converge at parametric rates. Instead, we make only fourth-root rate assumptions like those in the double machine learning literature. This result applies to conventional propensity-based trimming as well and thus may be of independent interest. Finally, we propose a bootstrap-based method for constructing simultaneously valid confidence intervals for multiple trimmed sub-populations, which are valuable for navigating the trade-off between sample size and variance reduction inherent in trimming. We validate our methods in simulation, on the 2007-2008 National Health and Nutrition Examination Survey, and on a semi-synthetic Medicare dataset and find promising results in all settings.

The Influence of Project-Based Complex Instruction (CI) Collaborative Learning Models and Learning Motivation on Elementary School Students’ Problem Solving Skill

The problems solving skills of class V students in mathematics learning are still relatively low. One of the things that causes this is the teacher's tendency to carry out learning conventionally through lecture, question and answer and assignment methods. Teachers have a dominant role in finding, processing and explaining the material being taught. Students are not given the freedom to elaborate on the material taught through collaboration with their peers. The objectives of this research are: to describe the influence of the project-based CI Collaborative Learning Model on students' problems solving skills; Describe the interaction between the project-based CI Collaborative Learning Model and learning motivation on students' problems solving skills. The research method used was Quasi Experiment with a Nonequivalent Pretest-Posttest Only Control Group Design. The research instruments used were problem solving skills tests and learning motivation questionnaires. The results of the homogeneity test and normality test of problems solving skill show that the sample variance is homogeneous and normally distributed. After fulfilling the prerequisite tests, a hypothesis test is carried out and it can be concluded that the project-based CI Collaborative Learning Model has a significant effect on students' problems solving skills. Apart from that, there is no interaction between the project-based CI Collaborative Learning Model and learning motivation on students' problems solving skills

Improving data interpretability with new differential sample variance gene set tests.

Gene set analysis methods have played a major role in generating biological interpretations from omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression and methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes. We use ranking schemes based on minimum spanning tree to generalize the Cramer-Von Mises and Anderson-Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterize these methods in addition to two methods developed earlier using simulation results with different parameters. We apply the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to heterogeneous differences. Our results show that methods designed to detect differential sample variance are able to detect specific hallmark signaling pathways associated with the two compared phenotypes as documented in available literature. The results in this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the developed methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets.

The rate and contribution of mergers to mass assembly from NIRCam observations of galaxy candidates up to 13.3 billion years ago

ABSTRACT We present an analysis of the galaxy merger rate in the redshift range $4.0\lt z\lt 9.0$ (i.e. about 1.5 to 0.5 Gyr after the big bang) based on visually identified galaxy mergers from morphological parameter analysis. Our data set is based on high-resolution NIRCam JWST data (a combination of F150W and F2000W broad-band filters) in the low-to-moderate magnification ($\mu \lt 2$) regions of the Abell 2744 cluster field. From a parent set of 675 galaxies $(M_{U}\in [-26.6,-17.9])$, we identify 64 merger candidates from the Gini, $M_{20}$ and asymmetry morphological parameters, leading to a merger fraction $f_m=0.11\pm 0.04$. There is no evidence of redshift evolution of $f_m$ even at the highest redshift considered, thus extending well into the epoch of reionization the constant trend seen previously at $z\lesssim 6$. Furthermore, we investigate any potential redshift-dependent differences in the specific star formation rates between mergers and non-mergers. Our analysis reveals no significant correlation in this regard, with deviations in the studied redshift range typically falling within $(1-1.5)\sigma$ from the null hypothesis that can be attributed to sample variance and measurement errors. Finally, we also demonstrate that the classification of a merging system is robust with respect to the observed (and equivalently rest frame) wavelength of the high-quality JWST broad-band images used. This preliminary study highlights the potential for progress in quantifying galaxy assembly through mergers during the epoch of reionization, with significant sample size growth expected from upcoming large JWST infrared imaging data sets.

Modeling optical systematics for the Taurus CMB experiment

We simulate a variety of optical systematics for Taurus, a balloon-borne cosmic microwave background (CMB) polarisation experiment, to assess their impact on large-scale E-mode polarisation measurements and constraints of the optical depth to reionisation τ.We model a one-month flight of Taurus from Wanaka, New Zealand aboard a super-pressure balloon (SPB).We simulate night-time scans of both the CMB and dust foregrounds in the 150 GHz band, one of Taurus's four observing bands.We consider a variety of possible systematics that may affect Taurus's observations, including non-gaussian beams, pointing reconstruction error, and half-wave plate (HWP) non-idealities.For each of these, we evaluate the residual power in the difference between maps simulated with and without the systematic, and compare this to the expected signal level corresponding to Taurus's science goals.Our results indicate that most of the HWP-related systematics can be mitigated to be smaller than sample variance by calibrating with Planck's TT spectrum and using an achromatic HWP model, with a preference for five layers of sapphire to ensure good systematic control.However, additional beam characterization will be required to mitigate far-sidelobe pickup from dust on larger scales.

Properties of risk aversion estimated from portfolio weights

While risk tolerance is often elicited using questionnaire-based instruments, in this paper, we evaluate the merits of an inversion-based technique, wherein risk aversion parameters are inferred from an individual’s portfolio holdings and a sequence of realized returns. We obtain expressions for the finite sample and asymptotic variance of the estimated risk aversion parameter under the inversion approach with a single risky asset, demonstrating that confidence intervals for parameter estimates are relatively wide. Extending the analysis, we show that inferring risk aversion from multiple risky assets does not typically serve to reduce the estimated parameter variance, but rather propagates estimation error.

Prediction of additive genetic variances of descendants for complex families based on Mendelian sampling variances.

The ability to predict the outcome of selection and mating decisions enables breeders to make strategically better selection decisions. To improve genetic progress, those individuals need to be selected whose offspring can be expected to show high genetic variance next to high breeding values. Previously published approaches enable to predict the variance of descendants of 2 future generations for up to 4 founding haplotypes, or 2 outbred individuals, based on phased genotypes, allele effects, and recombination frequencies. The purpose of this study was to develop a general approach for the analytical calculation of the genetic variance in any future generation. The core development is an equation for the prediction of the variance of double haploid lines, under the assumption of no selection and negligible drift, stemming from an arbitrary number of founder haplotypes. This double haploid variance can be decomposed into gametic Mendelian sampling variances (MSVs) of ancestors of the double haploid lines allowing usage for non-double haploid genotypes that enables application in animal breeding programs as well as in plant breeding programs. Together with the breeding values of the founders, the gametic MSV may be used in new selection criteria. We present our idea of such a criterion that describes the genetic level of selected individuals in 4 generations. Since breeding programs do select, the assumption made for predicting variances is clearly violated, which decreases the accuracy of predicted gametic MSV caused by changes in allele frequency and linkage disequilibrium. Despite violating the assumption, we found high predictive correlations of our criterion to the true genetic level that was obtained by means of simulation for the "corn" and "cattle" genome models tested in this study (0.90 and 0.97). In practice, the genotype phases, genetic map, and allele effects all need to be estimated meaning inaccuracies in their estimation will lead to inaccurate variance prediction. Investigation of variance prediction accuracy when input parameters are estimated was not part of this study.

Teacher-centered analysis with TIMSS and PIRLS data: weighting approaches, accuracy, and precision

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights.

FERN: is it possible to conduct a randomised controlled trial of intervention or expectant management for early-onset selective fetal growth restriction in monochorionic twin pregnancy – protocol for a prospective multicentre mixed-methods feasibility study

IntroductionSelective fetal growth restriction (sFGR) in monochorionic twin pregnancy, defined as an estimated fetal weight (EFW) of one twin <10th centile and EFW discordance ≥25%, is associated with stillbirth and...

Adaptive Multiple Comparisons With the Best.

Subset selection methods aim to choose a nonempty subset of populations including a best population with some prespecified probability. An example application involves location parameters that quantify yields in agriculture to select the best wheat variety. This is quite different from variable selection problems, for instance, inregression. Unfortunately, subset selection methods can become very conservative when the parameter configuration is not least favorable. This will lead to a selection of many non-best populations, making the set of selected populations less informative. To solve this issue, we propose less conservative adaptive approaches based on estimating the number of best populations. We also discuss variants of our adaptive approaches that are applicable when the sample sizes and/or variances differ between populations. Using simulations, we show that our methods yield a desirable performance. As an illustration of potential gains, we apply them to two real datasets, one on the yield of wheat varieties and the other obtained via genome sequencing of repeated samples.

ZTF SN Ia DR2: Peculiar velocities' impact on the Hubble diagram

Type Ia supernovae ( are used to determine the distance-redshift relation and build the Hubble diagram. Neglecting their host-galaxy peculiar velocities (PVs) may bias the measurement of cosmological parameters. The smaller the redshift, the larger the effect is. We used realistic simulations of observed by the Zwicky Transient Facility (ZTF) to investigate the effect of different methods of taking PVs into account. We studied the impact of neglecting galaxy PVs and their correlations in an analysis of the Hubble diagram. We find that it is necessary to use the PV full covariance matrix computed from the velocity power spectrum to take the sample variance into account. Considering the results we have obtained using simulations, we determine the PV systematic effects in the context of the ZTF SN Ia DR2 sample. We determine the PV impact on the intercept of the Hubble diagram, $a_B$, which is directly linked to the measurement of $H_0$. We show that not taking into account PVs and their correlations results in a shift in the $H_0$ value of about $1.0$ and a slight underestimation of the $H_0$ error bar.