Statistical Matching Research Articles

First fracture in rheumatoid arthritis: analysis by fracture site, gender, age, and comorbidities.

Rheumatoid arthritis RA is a potentially devastating osteoimmunological disorder, predisposing to osteoporosis (OP), fragility fracture (FF), and major osteoporotic fractures (MOF). As few studies incorporate statistical matching, comorbidity and non-MOF sites, we compared the incidence of first FF, MOF, and non-MOF in RA patients with a matched control cohort adjusting for comorbidities. This longitudinal cohort study uses routinely collected administrative data from the West Australian Rheumatic Disease Epidemiological Registry (WARDER) between 1980 and 2015. RA patients, as defined using International Classification of Disease (ICD) codes, were compared to hospitalised patients free of rheumatic disease. Case-control matching adjusted for age, gender, and comorbidities (Charlson Comorbidity Index). Incidence rates (IR) per 1000 person years (PY) with 95% confidence intervals (CI) were compared by incidence rate ratios (IRR). In RA patients from 2000 to 2010, the first fracture IR was 18.3 (15.7-21.2) for an IRR of 1.32 (1.10-1.60). Upper limb, lower limb, and axial IR were 5.56 (95% CI 4.18-7.26), 10.60 (95% CI 8.66-12.87), and 2.47 (95% CI 2.58-3.68) with IRR of 1.18 (95% CI 0.84-1.65), 1.44 (95% CI 1.19-1.86), and 1.01 (95% CI 0.61-1.63) respectively. The first fracture IR increased 6years before first RA hospital record (RR 1.58, CI 1.05-2.39). After age, gender, and comorbidity adjustment, RA is associated with a 32% higher incidence of first fracture, increased MOF, and a fracture incidence that is already increased before a first recorded RA diagnosis. This suggests a need for early attention to prevention of all fractures in RA patients.

Research on the railway multi-source homonymous geographical entity matching algorithm based on dynamic time warping

This study aims to explore an efficient technique for matching multisource homonymous geographical entities in railways to address the identification issues of homonymous geographical entities. Focusing on railway line vector spatial data, this research investigates the matching problem of multisource homonymous geographical entities. Building on statistical feature matching of attribute data, a curve similarity calculation method based on the DTW algorithm is designed to achieve better local elastic matching, overcoming the limitations of the Fréchet algorithm. The empirical study utilizes railway line layer data from two data sources within Beijing’s jurisdiction, fusing 6237 segment lines from source 2 with 105 long lines from source 1. The structural comparison between the two data sources is conducted through statistical methods, applying cosine similarity and the maximum similarity value of TF-IDF for text similarity calculation. Finally, Python is used to implement the DTW algorithm for curve similarity. The experimental results show an average DTW distance of 3.92, a standard deviation of 4.63, and a mode of 0.005. Similarity measurement results indicate that 95.53% of records are within the predetermined threshold, demonstrating the effectiveness and applicability of the method. The findings significantly enhance the accuracy of railway data matching, promoting the informatization of the railway industry, and hold substantial significance for improving railway operational efficiency and system performance.

The impact of startup subsidies on survival, growth, and stakeholder value

ABSTRACT Although the effects of subsidies on the survival, financial performance, and behavioral changes of startups have been extensively studied, their influence on economic value creation and distribution to stakeholders remains underexplored. To address this gap and mitigate publication bias, we examined the entire number of subsidies received by all Spanish startups at founding (51,495) over a decade, analyzing their outcomes 5 years after inception. We employed statistical matching methods controlling for the startups’ founding conditions. The study revealed that subsidies have a positive impact on startups’ growth and provide a significantly higher likelihood of survival; however, no significant effect on profitability was observed. Receiving a subsidy should be considered alongside other factors in explanatory models of business survival. Subsidies are associated with increased economic value creation and distribution to employees but not to the government through taxes. These findings underscore the importance of understanding the long-term consequences of subsidies on stakeholders.

The VISCACHA Survey – XI. Benchmarking SIESTA: a new synthetic CMD fitting code

ABSTRACT We present a novel code, named SIESTA (Statistical matchIng between rEal and Synthetic sTellar popuLations), designed for performing statistical isochrone fitting to colour–magnitude diagrams (CMDs) of single stellar populations by leveraging comparisons between the observed stellar distribution and predictions from synthetic populations, simulated on top of a grid of isochrones. These synthetic populations encompass determinant factors such as the cluster’s initial mass function (IMF), the presence of non-resolved binaries, as well as the expected photometric errors, and observational completeness (or the observed luminosity function). Employing Markov Chain Monte Carlo within a Bayesian framework, SIESTA allows for the determination of a cluster’s age, metallicity, distance, colour excess, and binary fraction (with masses exceeding a certain ratio). In this study, we rigorously benchmark the SIESTA code utilizing synthetic populations and evaluate its performance against observations from the VISCACHA Survey in the Small Magellanic Cloud, focusing on five star clusters: Lindsay 114, NGC 152, Lindsay 91, Lindsay 113, and NGC 121. These clusters were chosen for their diverse age range, spanning from 0.04 to 10 Gyr. Our findings demonstrate the capability of the SIESTA code to accurately represent the observed CMDs of these clusters. Furthermore, we compare the results obtained with SIESTA to previous characterizations of these clusters, highlighting the consistency between the derived metallicity and spectroscopic determinations from various sources.

Insulin Resistance and Impaired Insulin Secretion Predict Incident Diabetes: A Statistical Matching Application to the Two Korean Nationwide, Population-Representative Cohorts.

To evaluate whether insulin resistance and impaired insulin secretion are useful predictors of incident diabetes in Koreans using nationwide population-representative data to enhance data privacy. This study analyzed the data of individuals without diabetes aged >40 years from the Korea National Health and Nutrition Examination Survey (KNHANES) 2007-2010 and 2015 and the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS). Owing to privacy concerns, these databases cannot be linked using direct identifiers. Therefore, we generated 10 synthetic datasets, followed by statistical matching with the NHIS-HEALS. Homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of β-cell function (HOMA-β) were used as indicators of insulin resistance and insulin secretory function, respectively, and diabetes onset was captured in NHIS-HEALS. A median of 4,580 (range, 4,463 to 4,761) adults were included in the analyses after statistical matching of 10 synthetic KNHANES and NHIS-HEALS datasets. During a mean follow-up duration of 5.8 years, a median of 4.7% (range, 4.3% to 5.0%) of the participants developed diabetes. Compared to the reference low-HOMA-IR/high-HOMA-β group, the high-HOMA-IR/low- HOMA-β group had the highest risk of diabetes, followed by high-HOMA-IR/high-HOMA-β group and low-HOMA-IR/low- HOMA-β group (median adjusted hazard ratio [ranges]: 3.36 [1.86 to 6.05], 1.81 [1.01 to 3.22], and 1.68 [0.93 to 3.04], respectively). Insulin resistance and impaired insulin secretion are robust predictors of diabetes in the Korean population. A retrospective cohort constructed by combining cross-sectional synthetic and longitudinal claims-based cohort data through statistical matching may be a reliable resource for studying the natural history of diabetes.

Game theory in the classroom: low cooperative relationships identify bullying patterns in elementary schools

Cooperation and bullying have a subtle yet important interaction that influences the social dynamics in elementary school classrooms. We investigate this interplay in a large sample of 1112 students across 47 public primary classrooms in Chile. Using a video game interface to create a dyadic, non-anonymous social dilemma, we map the cooperative social network within each classroom. In addition, we collect peer nomination data and use the Illinois Bullying Scale to categorize students as bullies, victims, or bully victims. Our results indicate that low levels of received cooperation significantly increase the likelihood of students being identified with the dual role of both bully and victim, known as the bully-victim profile. This negative relationship remains robust even after controlling for demographic and classroom context variables using multilevel regression models and is consistent when employing causal inference techniques such as statistical matching. We propose that the relationship between received cooperation and the bully-victim profile stems from the capacity of received cooperation to capture key factors influencing social relationships among students, such as popularity, prosociality, GPA, and aggressiveness. Our study contributes to the understanding of human interaction in educational settings and it offers a new framework for targeted interventions in primary education, providing insights for future educational policies and practices.

Enhancing Critical Thinking and Learning Autonomy Through the Integration of Catur Pramana Approach in Natural and Social Sciences Interactive Learning Materials

This research investigates the influence of integrating interactive teaching materials based on Catur pramana principles on elementary school students' critical thinking and learning autonomy in Natural and Social Sciences (IPAS). This study employed interactive learning material, critical thinking, learning autonomy, and catur pramana theories. Employing a Quasi-Experimental design with a post-test-only control group design, two Grade IV classes were selected using a statistical matching technique. Data were collected through pre- and post-tests measuring critical thinking abilities and questionnaires assessing learning autonomy. The test and questionnaire showed that the experimental group had better critical thinking and learning autonomy than the control group. Besides, the quantitative analysis using descriptive statistics and MANOVA also revealed significant effects of integrating Catur pramana-oriented interactive teaching materials on students' critical thinking and learning autonomy in IPAS subjects. This study concludes that implementing interactive teaching materials effectively improves elementary students' critical thinking and learning autonomy. The findings suggest that integrating these materials presents a promising approach to enhancing elementary school students' critical thinking and learning autonomy.

Effect of acupuncture intervention time on the therapeutic effect in patients with sudden hearing loss.

To observe the clinical efficacy of acupuncture intervention at different time for patients with sudden hearing loss. According to the timing of acupuncture intervention, 86 patients were divided into early exposure group (n=43) and late exposure group (n=43) . The early exposure group was given acupuncture treatment within 14 days of onset, and the late exposure group was given acupuncture treatment after 14 days of onset. After propensity score matching (PSM, a statistical matching technique for observational data) processing by using SPSS26.0 software, outcomes of 30 cases in the early exposure group and 30 cases in the late exposure group were analyzed. In addition to receiving basic treatment with drugs for vascular dilatation, thrombolysis, nourishing nerve, etc., all patients of the two groups were treated with neck acupuncture ("Neck Seven Meridian Lines" acupuncture), once every other day except Sundays, for a total of 12 time. Before, after the treatment and 3 months after the treatment, the total score of the Tinnitus Handicap Inventory (THI, 0, 2 and 4 points for each of the 25 items, total scores = 100 points) scale was used to evaluate the improvement of tinnitus symptoms caused by hearing loss. The clinical therapeutic effect was evaluated according to the efficacy grading criteria in the Guidelines for Diagnosis and Treatment of Sudden Deafness (2015) and the changes of pure tone audiometry curve. Multivariate Logistic regression was used to analyze the effect of factors that might influence efficacy before propensity score matching. The THI scores of patients in both groups decreased strikingly after the treatment and 3 months' follow-up (P<0.05). Compared with the same time-points of the late exposure group, the total THI scores of post-treatment and 3 months' follow-up were evidently lower in the early exposure group (P<0.05). The effective rate of the early exposure group (22/30, 80.00%) was significantly higher (P<0.05) than that of the late exposure group (16/30, 53.33%). The classification of sudden deafness and the application of traditional Chinese medicine in this study were not independent factors affecting the total effective rate. The time point of acupuncture intervention is an important factor affecting the effect on hearing and tinnitus disability of patients with sudden deafness. The earlier acupuncture treatment is accepted, the better the therapeutic effect is.

Factors behind the resilience of rural startups

Rural areas are characterized by sparse population, low population density, remoteness, and population aging, which can discourage startups and undermine their financial performance, as the theory of urban agglomeration economies suggests. However, many studies have shown that companies created in rural areas survive longer than those created in urban areas, which seems contradictory. This paper aims to identify the factors that explain this rural resilience. We analyzed all of the startups founded in Spain over 10 years. We used statistical matching methods to test for the effect of the location while controlling for founding conditions and environmental factors. Our study confirmed that few startups are created in rural areas and they grow less, but their survival rate is slightly higher than those established in urban areas. We found that high subsidies, low operating costs, high labor intensity, a slow but low-risk growth pattern, and other foundational conditions and external factors contribute to explaining rural resilience. Nonetheless, additional factors, including the traits of rural entrepreneurs and the lack of alternatives to bankruptcy, as proposed by the entrepreneurial opportunity theory, also matter.

Flat rent price prediction in Berlin with web scraping

Internet data pose a challenge to the traditional system of official statistics, which relies on more conventional sources such as surveys and registers, not readily adaptable to rapid changes. Expanding this system to include internet data is currently at an experimental stage, exploring these sources’ potentials and benefits. This paper describes a project conducted within the ESSnet Trusted Smart Statistics – Web Intelligence Network framework. It investigates the use of online apartment listings to analyze the rental market. We used web scraping to extract information from two online real estate portals for flats in the city of Berlin. Using this data, we developed a model to predict rental prices per square meter based on the accommodation’s features and location within the city. We detected offers which appear in both portals by means of statistical matching and removed duplicate offers. Missing values were treated by multiple imputation. The prediction model is a semi-parametric approach where the postal districts are used to describe the location effect. Comparisons with microcensus results and the local rent index reveal significant differences between the market of online flat offers and the stock of existing flat contracts. Interested readers will find the commented programming code in the internet supplement.

Using Multiple Data Sources to Learn about the Race and Ethnicity of Taxpayers

Recently, studying income by race and ethnicity has gained interest among policymakers. The Congressional Budget Office (CBO) regularly reports on the distribution of household income, using a statistically matched dataset with survey and administrative tax data to account for the household resources for both tax filers and nonfilers. CBO has not evaluated whether the statistical match preserves the distribution of income by race and ethnicity reported in the survey. This preliminary analysis of CBO's statistically matched data by race and ethnicity uses information from survey data linked to administrative tax data at the individual level from the US Census Bureau.

Measuring The Impact of Special Economic Zone (SEZ) Arun Lhokseumawe on the Sustainability of its Peripheral Area

The impact of special economic zones (SEZ) on the sustainability of their peripheral areas is rarely measured and discussed. This research was conducted to determine the sustainability level of the Arun Lhokseumawe SEZ generated to its surrounding area before and after the exhaustion of its natural gas resources. The statistical matching method and the Moran index used for the analysis involved 14 sustainability variables representing social, economic, and environmental dimensions. The method used includes building the sustainability index using village level data, statistical matching to find evidence of impact, and determination of the Moran index to describe its spatial patterns. Additionally, the index was used to describe the policy implication based on the current SEZ sustainability level. The results show that there is no upward trend in the sustainability index after three years of operation, except for an increase in the SEZ (inside) in 2018 which then declines again in 2021. This figure is lower than before natural gas depletion. It was found that, in 2008, the spatial pattern of the high sustainability index mainly spread to areas outside the Arun Lhokseumawe industrial area. Meanwhile, in 2021, the spatial pattern of a high sustainability index clustered near the Arun Lhokseumawe special economic zone.

The effect of a political crisis on performance of community forests and protected areas in Madagascar

Understanding the effectiveness of conservation interventions during times of political instability is important given how much of the world’s biodiversity is concentrated in politically fragile nations. Here, we investigate the effect of a political crisis on the relative performance of community managed forests versus protected areas in terms of reducing deforestation in Madagascar, a biodiversity hotspot. We use remotely sensed data and statistical matching within an event study design to isolate the effect of the crisis and post-crisis period on performance. Annual rates of deforestation accelerated at the end of the crisis and were higher in community forests than in protected areas. After controlling for differences in location and other confounding variables, we find no difference in performance during the crisis, but community-managed forests performed worse in post-crisis years. These findings suggest that, as a political crisis subsides and deforestation pressures intensify, community-based conservation may be less resilient than state protection.

ACQ: Improving generative data-free quantization via attention correction

Data-free quantization aims to achieve model quantization without accessing any authentic sample. It is significant in an application-oriented context involving data privacy. Converting noise vectors into synthetic samples through a generator is a popular data-free quantization method, which is called generative data-free quantization. However, there is a difference in attention map between synthetic samples and authentic samples. This is always ignored and restricts the quantization performance. First, since synthetic samples of the same class are prone to have homogenous attention, the quantized network can only learn limited intra-class visual features. Second, synthetic samples in eval mode and training mode exhibit different attention. Hence, the statistical distribution matching tends to be inaccurate. ACQ is proposed in this paper to fix the attention of synthetic samples. Regarding intra-class attention homogeneity, we introduce an attention center matching loss aimed at achieving coarse-grained matching of attention of synthetic samples. Additionally, we have devised an adversarial loss based on pairs of samples with identical conditions. On one hand, this mechanism prevents the generator from mode collapse due to excessive attention on conditional information. On the other hand, it augments the separation between intra-class samples, thus further enhancing intra-class attention diversity. To improve the attention similarity of synthetic samples in different network modes, we introduce a consistency penalty to guarantee accurate statistical distribution matching. The experimental results demonstrate that ACQ effectively improves the attention problems of synthetic samples. Under various training settings, ACQ achieves the best quantization performance. For the 4-bit quantization of Resnet18 and Resnet50, ACQ reaches 67.55 % and 72.23 % accuracy, respectively.

Stable optimisation-based scenario generation via game theoretic approach

Systematic scenario generation (SG) methods have emerged as an invaluable tool to handle uncertainty towards the efficient solution of stochastic programming (SP) problems. The quality of SG methods depends on their consistency to generate scenario sets which guarantee stability on solving SPs and lead to stochastic solutions of good quality. In this context, we delve into the optimisation-based Distribution and Moment Matching Problem (DMP) for scenario generation and propose a game theoretic approach which is formulated as a Mixed-Integer Linear Programming (MILP) model. Nash bargaining approach is employed and the terms of the objective function regarding the statistical matching of the DMP are considered as players. Results from a capacity planning case study highlight the quality of the stochastic solutions obtained using MILP DMP models for scenario generation. Furthermore, the proposed game theoretic extension of DMP enhances in-sample and out-of-sample stability with respect to the challenging problem of user-defined parameters variability.

Development of anisotropic force fields for homopolymer melts at the mesoscale.

With the aim of producing realistic coarse-grained models of homopolymers, we introduce a tabulated backbone-oriented anisotropic potential. The parameters of the model are optimized using statistical trajectory matching. The impact of grain anisotropy is evaluated at different coarse-graining levels using cis-polybutadiene as a test case. We show that, at the same time, tuning the aspect ratio of the grains can lead to a better density and structure and may reduce the unphysical bond crossings by up to 90%, without increasing the computation time too much and thereby jeopardizing the main advantage of coarse-grained models.

The impact of differentiated learning materials on students’ understanding of nationalism and global diversity

This study aims to determine the differentiated learning materials' impact on students' understanding of nationalism and global diversity. This research was a quasi-experimental study conducted by following a post-test-only control group design. Two classes from different schools were selected as research samples using a statistical matching technique. The researchers collected the data using tests and analyzed them using descriptive statistics and an independent t-test to achieve the study's objective. The results of the descriptive statistics showed that the experimental group had a higher score than the control group. It can be seen from the mean, minimum and maximum scores of the two groups. This result demonstrates that students who used varied learning resources had a greater comprehension of nationalism and global diversity. Furthermore, the independent t-test result confirmed that the differences in understanding were significant. In terms of the effect size, it confirms that differentiated instruction greatly affects students' understanding of nationalism and global diversity. Thus, this study concluded that differentiated learning materials affect students' understanding of nationalism and global diversity significantly. Thus, the researchers promote differentiated learning materials for teaching nationalism and global diversity based on the study results. Moreover, differentiated instruction may also be used for teaching different learning materials.

The role of dairy consumption in the relationship between wealth and early life physical growth in India: evidence from multiple national surveys

IntroductionPrevalence of undernutrition continues to be high in India and low household wealth is consistently associated with undernutrition. This association could be modified through improved dietary intake, including dairy consumption in young children. The beneficial effect of dairy on child growth has not been explored at a national level in India. The present analyses aimed to evaluate the direct and indirect (modifying association of household level per adult female equivalent milk and milk product consumption) associations between household wealth index on height for age (HAZ) and weight for age (WAZ) in 6-59 months old Indian children using data from of nationally representative surveys.MethodsTwo triangulated datasets of two rounds of National Family Health Survey, (NFHS-3 and 4) and food expenditure (National Sample Survey, NSS61 and 68) surveys, were produced by statistical matching of households using Non-Iterative Bayesian Approach to Statistical Matching technique. A Directed Acyclic Graph was constructed to map the pathways in the relationship of household wealth with HAZ and WAZ based on literature. The direct association of wealth index and its indirect association through per adult female equivalent dairy consumption on HAZ and WAZ were estimated using separate path models for each round of the surveys.ResultsWealth index was directly associated with HAZ and WAZ in both the rounds, but the association decreased from NFHS-3 (βHAZ: 0.145; 95% CI: 0.129, 0.16) to NFHS-4 (βHAZ: 0.102; 95%CI: 0.093, 0.11). Adult female equivalent milk intake (increase of 10gm/day) was associated with higher HAZ (β_NFHS-3=0.001;95% CI: 0, 0.002; β_NFHS-4=0.002;95% CI: 0.002, 0.003) but had no association with WAZ. The indirect association of wealth with HAZ through dairy consumption was 2-fold higher in NFHS-4 compared to NFHS-3.ConclusionsThe analysis of triangulated survey data shows that household level per- adult female equivalent dairy consumption positively modified the association between wealth index and HAZ, suggesting that regular inclusion of milk and milk products in the diets of children from households across all wealth quintiles could improve linear growth in this population.

Statistical matching using kernel canonical correlation analysis and super-organizing map

The potential to study and improve different aspects of our lives is ever growing thanks to the abundance of data available in today’s modern society. Scientists and researchers often need to analyze data from different sources; the observations, which only share a subset of the variables, cannot always be paired to detect common individuals. This is the case, for example, when the information required to study a certain phenomenon is coming from different sample surveys. Statistical matching is a common practice to combine these data sets. In this paper, we investigate and extend to statistical matching two methods based on Kernel Canonical Correlation Analysis (KCCA) and Super-Organizing Map (Super-OM). These methods are designed to deal with various variable types, sample weights and incompatibilities among categorical variables. In the first case, we use KCCA, a non-linear extension of CCA, to create canonical variables that we can compare in the two data sets. In the second case, Super-OM uses organizing maps to create subgroups of individuals who share the same characteristics. We use the 2017 Belgian Statistics on Income and Living Conditions (SILC) and we compare the performance of the proposed statistical matching methods by means of a cross-validation technique, as if the data were available from two separate sources. The results indicate that our proposed methods are superior to existing methods because they preserve the distribution of generated variables while also providing good predictions. Existing methods typically only achieve one or the other. These new techniques open the door to improving statistical matching in other applications such as medicine, economics, …

Point cloud registration algorithm based on statistical local feature description and matching

Point cloud registration algorithm based on statistical local feature description and matching