Original Estimates Research Articles

Re-election prospects in the local contest in Slovakia: discussing influencing roles of socio-economic conditions of the electorate and municipal finances

ABSTRACT The paper examines the re-election prospects at the local level in Slovakia. Using a database containing all 2,926 Slovak municipalities, we conducted research on local election results in the period of 2005–2022. Our paper got original estimates for the Slovakian reality. First, we observed that the aged electorate tends to promote the re-election of incumbents, as well as a higher number of male residents or a higher volume of municipal expenditures. Finally, we also observed that there is a gender gap in mayors’ re-election due to a higher difficulty of female mayors being re-elected. Being an independent mayor or belonging to a right-wing party, serving in a municipality with higher levels of unemployment, or in a large municipality in terms of the administrative area also reduces the likelihood of re-election in Slovakia’s municipal polls.

Examining the replicability of online experiments selected by a decision market.

Here we test the feasibility of using decision markets to select studies for replication and provide evidence about the replicability of online experiments. Social scientists (n = 162) traded on the outcome of close replications of 41 systematically selected MTurk social science experiments published in PNAS 2015-2018, knowing that the 12 studies with the lowest and the 12 with the highest final market prices would be selected for replication, along with 2 randomly selected studies. The replication rate, based on the statistical significance indicator, was 83% for the top-12 and 33% for the bottom-12 group. Overall, 54% of the studies were successfully replicated, with replication effect size estimates averaging 45% of the original effect size estimates. The replication rate varied between 54% and 62% for alternative replication indicators. The observed replicability of MTurk experiments is comparable to that of previous systematic replication projects involving laboratory experiments.

Mean square consistency and improved rate of convergence of generalized subsampling estimator for non‐stationary time series

In this article, we show the mean square consistency for a generalized subsampling estimator based on the aggregation of the mean, median, and trimmed mean of some subsampling estimators for general non‐stationary time series. Consistency requires standard assumptions, including the existence of moments and ‐mixing conditions. We apply our results to the Fourier coefficients of the autocovariance function of periodically correlated time series. Furthermore, as in the i.i.d. case, we show that the generalized subsampling estimator satisfies Bernstein inequality and concentrates at an improved rate (under the condition of no or small bias) compared with the original estimator. Finally, we illustrate our results with some simulation data examples.

Combined lifestyle factors and metabolic syndrome risk: a systematic review and meta-analysis.

The metabolic syndrome is a complex condition influenced by many factors including lifestyle. Recently, more and more studies explored the relationships between combined lifestyle factors (often measured as lifestyle scores/indices) and metabolic syndrome due to the co-occurrence of these factors. These scores/indices considered potential interactions among lifestyle factors, offering a more comprehensive understanding of their relationship with metabolic syndrome. However, no review/meta-analysis has been conducted to summarize existing evidence. Thus, this study aimed to synthesize the associations between lifestyle scores/indices and metabolic syndrome in cross-sectional and cohort studies. A literature search was performed in Embase and Medline. Multivariable-adjusted estimates were synthesized using random-effects models. In research where higher scores indicated better health, we used original estimates directly. In studies where higher scores denoted poorer health, we first calculated the coefficients and standard errors based on original estimates. Afterward, we reversed coefficients' directions and recalculated new estimates. Thus, the pooled estimates compared the healthiest with the least-healthy lifestyles (the highest vs. lowest scores/indices). Subgroup analyses were conducted based on study design, region, baseline time, baseline age, sex, health status, metabolic syndrome diagnosis, and lifestyles' number. Sensitivity analyses were performed by including only high-quality studies and employing leave-one-out analyses. Nineteen studies from 16 publications were included. Physical activity, diet, and smoking were the top three included lifestyle factors. Compared to participants with the least-healthy lifestyles, those with the healthiest lifestyles had a 43% lower metabolic syndrome risk (95% confidence interval = 0.41-0.73). In subgroup analyses, healthy lifestyle scores/indices were inversely associated with both metabolic syndrome prevalence in cross-sectional studies (Odds ratio = 0.62; 95% confidence interval = 0.51-0.73) and metabolic syndrome incidence in cohort studies (Odds ratio = 0.40; 95% confidence interval = 0.11-0.68). The inverse association was consistent in other subgroup and sensitivity analyses. Adherence to a healthy lifestyle pattern was beneficial to metabolic syndrome prevention.

A Modified Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization.

Mendelian randomization uses genetic variants as instrumental variables to estimate the causal effect of exposure on outcome from observational data. A common challenge in Mendelian randomization is that many genetic variants are only modestly or even weakly associated with the exposure of interest, a setting known as many weak instruments. Conventional methods, such as the popular inverse-variance weighted (IVW) estimator, could be heavily biased toward zero when the instrument strength is weak. To address this issue, the debiased IVW (dIVW) estimator and the penalized IVW (pIVW) estimator, which are shown to be robust to many weak instruments, were recently proposed. However, we find that the dIVW estimator tends to produce an exaggerated estimate of the causal effect, especially when the effective sample size is small. Although the pIVW estimator has better statistical properties, it is slightly more complex, and the idea behind this method is also a bit less intuitive. Therefore, we propose a modified debiased IVW (mdIVW) estimator that directly multiplies a shrinkage factor with the original dIVW estimator. After this simple modification, we prove that the mdIVW estimator not only has second-order bias with respect to the effective sample size, but also has smaller variance and mean squared error than the preceding two estimators. We then extend the proposed method to account for the presence of instrumental variable selection and balanced horizontal pleiotropy. We demonstrate the improvement of the mdIVW estimator over the competing ones through extensive simulation studies and real data analysis.

Bayesian blockwise inference for joint models of longitudinal and multistate data with application to longitudinal multimorbidity analysis.

Multistate models provide a useful framework for modelling complex event history data in clinical settings and have recently been extended to the joint modelling framework to appropriately handle endogenous longitudinal covariates, such as repeatedly measured biomarkers, which are informative about health status and disease progression. However, the practical application of such joint models faces considerable computational challenges. Motivated by a longitudinal multimorbidity analysis of large-scale UK health records, we introduce novel Bayesian inference approaches for these models that are capable of handling complex multistate processes and large datasets with straightforward implementation. These approaches decompose the original estimation task into smaller inference blocks, leveraging parallel computing and facilitating flexible model specification and comparison. Using extensive simulation studies, we show that the proposed approaches achieve satisfactory estimation accuracy, with notable gains in computational efficiency compared to the standard Bayesian estimation strategy. We illustrate our approaches by analysing the coevolution of routinely measured systolic blood pressure and the progression of three important chronic conditions, using a large dataset from the Clinical Practice Research Datalink Aurum database. Our analysis reveals distinct and previously lesser-known association structures between systolic blood pressure and different disease transitions.

Energy storage configuration method for distribution networks based on moment difference analysis

The integration of a high proportion of distributed PV into distribution networks can cause power backfeeding and voltage limit violations. This paper introduces moment difference analysis theory for distribution networks, reframing PV consumption as the balancing moment difference equations. When maximum node voltage approaches the upper limit, the moment difference between PV and load stabilizes to an approximately constant, termed the standard moment difference. The standard moment difference represents the limit of the network's capacity to consume distributed PV. Essentially, the PV moment is the target for integration, while the load moment serves as the resource to consume the PV moment. Based on this theory, a method for energy storage configuration is proposed. Simplifying a complex multi-branch distribution network into single-branch lines and solving linear equations determines the optimal storage configuration. This method was tested on the Jibei Power Grid and the IEEE 33 and 69 bus systems, increasing computational efficiency by 1611.47–4973.82 times compared to the PSO algorithm, while maintaining an error margin of less than 2.6 %. Furthermore, the discharging strategy reduces the ESS capacity to between 12.39 % and 31.69 % of the original estimate, thereby enhancing both the economic and practical appeal of the approach.

Validation Assessment of Privacy-Preserving Synthetic Electronic Health Record Data: Comparison of Original Versus Synthetic Data on Real-World COVID-19 Vaccine Effectiveness.

To assess the validity of privacy-preserving synthetic data by comparing results from synthetic versus original EHR data analysis. A published retrospective cohort study on real-world effectiveness of COVID-19 vaccines by Maccabi Healthcare Services in Israel was replicated using synthetic data generated from the same source, and the results were compared between synthetic versus original datasets. The endpoints included COVID-19 infection, symptomatic COVID-19 infection and hospitalization due to infection and were also assessed in several demographic and clinical subgroups. In comparing synthetic versus original data estimates, several metrices were utilized: standardized mean differences (SMD), decision agreement, estimate agreement, confidence interval overlap, and Wald test. Synthetic data were generated five times to assess the stability of results. The distribution of demographic and clinical characteristics demonstrated very small difference (< 0.01 SMD). In the comparison of vaccine effectiveness assessed in relative risk reduction between synthetic versus original data, there was a 100% decision agreement, 100% estimate agreement, and a high level of confidence interval overlap (88.7%-99.7%) in all five replicates across all subgroups. Similar findings were achieved in the assessment of vaccine effectiveness against symptomatic COVID-19 Infection. In the comparison of hazard ratios for COVID 19-related hospitalization and odds ratio for symptomatic COVID-19 Infection, the Wald tests suggested no significant difference between respective effect estimates in all five replicates for all patient subgroups but there were disagreements in estimate and decision metrices in some subgroups and replicates. Overall, comparison of synthetic versus original real-world data demonstrated good validity and reliability. Transparency on the process to generate high fidelity synthetic data and assurances of patient privacy are warranted.

Predicting imidazolium ionic liquid properties with a simple molecular volume-based SAFT-VR Mie approach

Each thermodynamic model requires a distinct set of parameters for each component and these parameters must be transferable beyond their original estimation domain to accurately predict a variety of thermodynamic properties. Previous work introduced and evaluated a novel parametrization method for the solubility of ionic liquids in CO2 or CH4 [Chem. Eng. Sci. 2024, 285, 119610]. This methodology provides a fast and efficient way to evaluate ionic liquids by requiring only their molecular volume. In this study, the approach is extended to include both pure ionic liquids, examining properties such as density, isobaric heat capacity, isobaric expansivity, isothermal compressibility, and speed of sound, as well as their mixtures with ethanol or water, focusing on speed of sound, and excess enthalpy. Ionic liquids are modeled as both non-associating and associating components. Overall, the approach demonstrates good estimates and holds potential for extension to other models and systems.

Estimation of dynamic Origin–Destination matrices in a railway transportation network integrating ticket sales and passenger count data

Accurately estimating Origin–Destination matrices is a pressing challenge in transportation management and urban planning. However, traditional methods like travel surveys have limitations in availability and comprehensiveness, which have been further exacerbated by the recent changes in mobility patterns induced by the COVID-19 pandemic. To address this issue, we focused on the Trenord railway network in Lombardy, Italy, and developed an innovative pipeline to integrate ticket and subscription sales and Automated Passenger Counting data using the Iterative Proportional Fitting algorithm. By effectively navigating the complexities of diverse and incomplete data sources, our approach showcases adaptability across various transportation contexts. Our research offers a valuable tool for operators, policymakers, and researchers, bridging the gap between data availability and the need for precise OD matrices. Additionally, we emphasise the potential of dynamic OD matrices and showcase methods for detecting anomalies in mobility trends, interpreting them in the context of events from the last months of 2022.

The Bayesian reservoir model of psychological regulation.

Social and behavioral scientists are increasingly interested the dynamics of the processes they study. Despite the wide array of processes studied, a fairly narrow set of models are applied to characterize dynamics within these processes. For social and behavioral research to take the next step in modeling dynamics, a wider variety of models need to be considered. The reservoir model is one model of psychological regulation that helps expand the models available (Deboeck & Bergeman, 2013). The present article implements the Bayesian reservoir model for both single time series and multilevel data. Simulation 1 compares the performance of the original version of the reservoir model fit using structural equation modeling (Deboeck & Bergeman, 2013) to the proposed Bayesian estimation approach. Simulation 2 expands this to a multilevel data scenario and compares this to the single-level version. The Bayesian estimation approach performs substantially better than the original estimation approach and produces low-bias estimates even with time series as short as 25 observations. Combining Bayesian estimation with a multilevel modeling approach allows for relatively unbiased estimation with sample sizes as small as 15 individuals and/or with time series as short as 15 observations. Finally, a substantive example is presented that applies the Bayesian reservoir model to perceived stress, examining how the model parameters relate to psychological variables commonly expected to relate to resilience. The current expansion of the reservoir model demonstrates the benefits of leveraging the combined strengths of Bayesian estimation and multilevel modeling, with new dynamic models that have been tailored to match the process of psychological regulation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Quantifying microbial DNA in metagenomes improves microbial trait estimation

Abstract Shotgun metagenomics is a powerful tool for studying the genomic traits of microbial community members, such as genome size, gene content, etc. While such traits can be used to better understand the ecology and evolution of microbial communities, the accuracy of their estimations can be critically influenced by both known and unknown factors. One factor that can bias trait estimations is the proportion of eukaryotic and viral DNA in a metagenome, as some bioinformatic tools assume that all DNA reads in a metagenome are bacterial or archaeal. Here, we add to a recent debate about the influence of eukaryotic DNA in the estimation of average genome size from a global soil sample dataset using a new bioinformatic tool. Contrary to what was assumed, our reanalysis of this dataset revealed that soil samples can contain a substantial proportion of non-microbial DNA, which severely inflated the original estimates of average genome size. Correcting for this bias significantly improves the statistical support for the negative relationship between average bacterial genome size and soil pH. These results highlight that metagenomes can contain large quantities of non-microbial DNA, and that new methods that correct for this can improve microbial trait estimation.

Enhanced Response Envelope via Envelope Regularization

The response envelope model provides substantial efficiency gains over the standard multivariate linear regression by identifying the material part of the response to the model and by excluding the immaterial part. In this article, we propose the enhanced response envelope by incorporating a novel envelope regularization term based on a nonconvex manifold formulation. It is shown that the enhanced response envelope can yield better prediction risk than the original envelope estimator. The enhanced response envelope naturally handles high-dimensional data for which the original response envelope is not serviceable without necessary remedies. In an asymptotic high-dimensional regime where the ratio of the number of predictors over the number of samples converges to a nonzero constant, we characterize the risk function and reveal an interesting double descent phenomenon for the envelope model. A simulation study confirms our main theoretical findings. Simulations and real data applications demonstrate that the enhanced response envelope does have significantly improved prediction performance over the original envelope method, especially when the number of predictors is close to or moderately larger than the number of samples. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Empirical evidence of Goodwin’s growth cycle in the Indian organised manufacturing sector

PurposeThe aim of the paper is to investigate Goodwin’s growth cycle in the Indian organised manufacturing industries.Design/methodology/approachThe methodology is based on bi-variate differential equation, econometrics model like log-linear regression and Autoregressive Distributed Lag model. An empirical investigation is conducted on data from the Annual Survey of Industries from 1980 to 2018 time period.FindingsThe results indicate that though the original Goodwin model estimates deviated from data estimates, its modified (neo-Goodwin) model are found to be equivalent to the data estimates. Moreover, in contrast to the original model, the capital accumulation rate (investment to profit ratio) is not assumed to be unitary in the modified Goodwin model. Furthermore, the labour market-led and cost effect conditions of the Goodwin cycle are empirically verified by investigating the interdependency between employment rate and wage share. Lastly, the short- and long-run Goodwin cycles are observed to be moving in anti-clockwise direction in the employment rate and wage share bi-dimensional plane, thus confirming the existence of profit-led distribution where wage share continuously reducing with high employment.Research limitations/implicationsThis study opens the discussion on application of capitalistic model in the emerging economy and also suggests to incorporate some theoretical models like Kaldorian, Keynesian, Kaleckian or Schumpetrian into the Goodwin cycle.Originality/valueThis is the first paper which empirically examines the capitalistic nature of Indian organised manufacturing industries through the lens of Goodwin growth cycle and then extend it to the Neo-Goodwin model by relaxing one of the unrealistic assumption regarding unitary investment to profit ratio.

Real-Time Estimation of Origin–Destination Matrices Using a Deep Neural Network for Digital Twins

The digital twin, a real-time replica of physical systems, has garnered attention as a promising tool to strategize and evaluate solutions for complex real-world issues. However, developing digital twins in the field of transportation faces significant challenges related to the real-time estimation of dynamic origin–destination (OD) matrices constrained by computation time. To bridge this gap, microscopic traffic simulations with real-time synchronization are being researched. Nonetheless, the computational issue persists, emphasizing the need for more efficient OD estimation methods. In this regard, our objective is to reduce computation time in simulation-based methods by developing a data-driven metamodel using a deep neural network. The proposed model serves to map the correlation between the OD matrix and detector data. This model simplifies the computational process using hidden layers, rather than calculating complex interactions between vehicles in the traffic simulation. Compared to conventional methods, we evaluate the capability to estimate a reasonable OD matrix within a restricted time and validate our approach using detector data from Daejeon City, South Korea. The findings indicate that by combining our data-driven metamodel with the simultaneous perturbation stochastic approximation, it becomes feasible to estimate a reasonable OD matrix within a stipulated time frame, compared to the conventional method. Given a 1-min time frame, the proposed method outperforms the conventional simulation-based method by improving the calibration performance of traffic flow by 44.5 percentage points. This paper proposes a practical and versatile approach for real-time OD estimation, laying the foundation for extending microscopic traffic simulation into the digital twin.

Improving estimation efficiency of case-cohort studies with interval-censored failure time data.

The case-cohort design is a commonly used cost-effective sampling strategy for large cohort studies, where some covariates are expensive to measure or obtain. In this paper, we consider regression analysis under a case-cohort study with interval-censored failure time data, where the failure time is only known to fall within an interval instead of being exactly observed. A common approach to analyzing data from a case-cohort study is the inverse probability weighting approach, where only subjects in the case-cohort sample are used in estimation, and the subjects are weighted based on the probability of inclusion into the case-cohort sample. This approach, though consistent, is generally inefficient as it does not incorporate information outside the case-cohort sample. To improve efficiency, we first develop a sieve maximum weighted likelihood estimator under the Cox model based on the case-cohort sample and then propose a procedure to update this estimator by using information in the full cohort. We show that the update estimator is consistent, asymptotically normal, and at least as efficient asthe original estimator. The proposed method can flexibly incorporate auxiliary variables to improve estimation efficiency. A weighted bootstrap procedure is employed for variance estimation. Simulation results indicate that the proposed method works well in practical situations. An application to a Phase 3 HIV vaccine efficacy trial is provided for illustration.

Replication study: Investigating the effects of maximal anaerobic fatigue on dynamic postural control using the Y-Balance Test

ObjectivesThe aim of this study was to replicate the study titled “Investigating the effects of maximal anaerobic fatigue on dynamic postural control using the Y-Balance Test” by Johnston et al. (2018) as part of a large replication project. This study aimed to determine the effects of maximal anaerobic fatigue on dynamic postural control using the Y-Balance Test, with a specific focus on anterior reach distance. DesignA single session intervention. MethodsForty male and female university students (age: 22 ± 3 years, height: 171.88 ± 8.96 cm, mass: 76.20 ± 14.31 kg) partaking in competitive sport, completed a Y-Balance Test protocol at 20, 10, and 0 min before a modified 60 s Wingate fatiguing protocol. Post-fatigue assessments were completed at 0, 10, and 20 min after the Wingate test. Replication outcomes included significance, direction, and effect size comparison using a z-test. ResultsA one-way repeated measures ANOVA showed a significant effect for maximal anaerobic fatigue on normalised anterior reach direction (F1.82, 69.03 = 7.16, p = 0.002, ηp2 = 0.159; 95 % CI: 0.025, 0.320). Therefore, we replicated the original findings for anterior reach distance in terms of statistical significance (F = 3.818, p = 0.025, ηp2 = 0.376; 95 % CI: 0.00, 0.62). The replication and original effect size estimates for anterior reach direction were compared using a z-test and were deemed compatible (z = 1.65, p = 0.05). ConclusionsOverall, we replicated the original study findings for the effect of maximal anaerobic fatigue on anterior reach distance in a Y-Balance Test.

Use of otolith microchemistry to determine natal origin for Silver Carp Hypophthalmichthys molitrix in the lower Mississippi River basin

Silver Carp (Hypophthalmichthys molitrix) populations have established and expanded throughout the lower Mississippi River basin (LMRB). Information pertaining to Silver Carp population mixing among rivers within the LMRB is lacking. Documented relations between Silver Carp otolith and river water barium (Ba) and strontium (Sr) microchemical signatures may enable estimation of origins of Silver Carp in the LMRB. Replicate water samples and otoliths from 308 Silver Carp were collected from the Cache, Arkansas, White, Yazoo, St. Francis, L’Anguille, and Mississippi rivers, and Merrisach Lake (situated along a canal connecting the lower reaches of the Arkansas and White rivers) within the LMRB. Water and carp otolith microchemical signatures exhibited consistent differences among water bodies. A classification and regression tree model exhibited 80% accuracy when assigning carp collected from the White, Arkansas, and Mississippi rivers based on fish-water microchemical signatures. Model accuracy decreased as smaller rivers were incorporated into models. Predicted natal origin based on otolith microchemical signatures suggested the White River (43%) and the lower Mississippi River (39%) were the likely origins for ~ 82% of the Silver Carp sampled. Despite the prevalence of adult Silver Carp within the Arkansas River system, fewer (18%) appeared to have originated there compared to the White and Mississippi rivers. Long-term water sampling and additional isotopic measurements may refine analyses to better determine the relative contributions of Silver Carp from the smaller river systems. Population mixing of Silver Carp among tributary rivers appears to be common within the LMRB, and removal efforts may benefit from evaluating the magnitude of fish movement and connectivity among rivers.

Area of origin estimation from multiple arbitrarily oriented surfaces using marker-guided structure from motion

Bloodstain pattern analysis plays a crucial role in forensic investigations. Projected patterns can offer valuable insights into the dynamics of crime scenes. In this paper, we propose and validate a novel approach that extends existing software, HemoVision, to analyze impact patterns that are distributed across multiple arbitrarily oriented surfaces. The proposed method integrates HemoVision’s marker-based system with structure from motion (SfM) techniques to reconstruct the three-dimensional geometry of impact patterns using only two-dimensional photographs. Controlled experiments were used to validate the proposed approach, demonstrating robustness in reconstruction accuracy with median translation errors below 3 mm and median angular errors below 0.2°, irrespective of imaging device or image resolution. Comparing the estimated areas origin to their known ground truth, the proposed method achieved an average total error of 8.12 cm, with the primary source of error being the vertical dimension. Despite this, the overall error remains well within the ranges of error reported in prior work. This study demonstrates that HemoVision can be used to analyze complex impact patterns using only two-dimensional photographs, providing forensic experts with an efficient and accessible tool for investigating intricate crime scenes involving multi-surface impact patterns.

How to Balance Prognostic Factors in Controlled Phase II Trials: Stratified Permuted Block Randomization or Minimization? An Analysis of Clinical Trials in Digestive Oncology.

In controlled phase II trials, major prognostic factors need to be well balanced between arms. The main procedures used are SPBR (Stratified Permuted Block Randomization) and minimization. First, we provide a systematic review of the treatment allocation procedure used in gastrointestinal oncology controlled phase II trials published in 2019. Second, we performed simulations using data from six phase II studies to measure the impacts of imbalances and bias on the efficacy estimations. From the 40 articles analyzed, all mentioned randomization in both the title and abstract, the median number of patients included was 109, and 77.5% were multicenter. Of the 27 studies that reported at least one stratification variable, 10 included the center as a stratification variable, 10 used minimization, 9 used SBR, and 8 were unspecified. In real data studies, the imbalance increased with the number of centers. The total and marginal imbalances were higher with SBR than with minimization, and the difference increased with the number of centers. The efficiency estimates per arm were close to the original trial estimate in both procedures. Minimization is often used in cases of numerous centers and guarantees better similarity between arms for stratification variables for total and marginal imbalances in phase II trials.