There are rare researches on the correlations between metals exposure and serum uric acid (SUA), and existing research has only investigated the single metal effect. This study aimed to investigate ...the combined effects of metal mixtures on SUA and hyperuricemia using three statistical models.
In this study, the data were extracted from three cycle years of the National Health and Nutrition Examination Survey (NHANES). Subsequently, generalized linear regression, weighted quantile regression (WQS) and Bayesian kernel machine regression (BKMR) models were fitted to evaluate the correlations between metal mixtures and both SUA and hyperuricemia.
Of 3926 participants included, 19.13% participants had hyperuricemia. It was found using multi-metals generalized linear regression models that there were positive correlations of arsenic and cadmium with both outcomes. The negative correlations were identified in cobalt, iodine, and manganese with SUA concentration, whereas only cobalt was negatively correlated with hyperuricemia. Based on the WQS regression model fitted in positive direction, it was suggested that the WQS indices were significantly correlated with SUA (β = 6.64, 95% CI: 3.14–10.13) and hyperuricemia (OR = 1.25, 95% CI: 1.08–1.44); however, the result achieved by using the model fitted in negative direction indicated that the WQS indices were only significantly correlated with SUA (β = -5.29, 95%CI: 8.02 ∼ −2.56). With the use of the BKMR model, a significant increasing trend between metal mixtures and hyperuricemia was found, while no significant overall effect of metal mixtures on SUA was identified. The predominant roles of arsenic, cadmium, and cobalt in the change of SUA and hyperuricemia risk were found using all three models.
The finding of this study revealed that metal mixtures might have a positive combined effect on hyperuricemia. The mutual verification of two outcomes using the three different models provided strong public health implications for protecting people from heavy metal pollution and preventing hyperuricemia.
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•The effects of exposure to a variety of metals on serum uric acid were examined and studied.•Novel methods were adopted to deal with high-dimensional exposures and verify each other.•The single and combined effects of different metals were explored.
We develop a Lagrange Multiplier (LM) test of neglected heterogeneity in dyadic models. The test statistic is derived by modifying Breusch and Pagan (1980)’s test. We establish the asymptotic ...distribution of the test statistic under the null using a novel martingale construction. We also consider the power of the LM test in generic panel models. Even though the test is motivated by random effects, we show that it has a power for detecting fixed effects as well. Finally, we examine how the estimation noise of the maximum likelihood estimator affects the asymptotic distribution of the test under the null, and show that such a noise may be ignored in large samples.
The world has suffered a lot from COVID-19 and is still on the verge of a new outbreak. The infected regions of coronavirus have been classified into four categories: SIRD model, (1) suspected, (2) ...infected, (3) recovered, and (4) deaths, where the COVID-19 transmission is evaluated using a stochastic model. A study in Pakistan modeled COVID-19 data using stochastic models like PRM and NBR. The findings were evaluated based on these models, as the country faces its third wave of the virus. Our study predicts COVID-19 casualties in Pakistan using a count data model. We’ve used a Poisson process, SIRD-type framework, and a stochastic model to find the solution. We took data from NCOC (National Command and Operation Center) website to choose the best prediction model based on all provinces of Pakistan, On the values of log L and AIC criteria. The best model among PRM and NBR is NBR because when over-dispersion happens; NBR is the best model for modelling the total suspected, infected, and recovered COVID-19 occurrences in Pakistan as it has the maximum log L and smallest AIC of the other count regression model. It was also observed that the active and critical cases positively and significantly affect COVID-19-related deaths in Pakistan using the NBR model.
Unsupervised feature selection always occupies a key position as a preprocessing in the tasks of classification or clustering due to the existence of extra essential features within high-dimensional ...data. Although lots of efforts have been made, the existing methods neglect to consider the redundancy of features, and thus select redundant features. In this brief, by virtue of a generalized uncorrelated constraint, we present an improved sparse regression model generalized uncorrelated regression model (GURM) for seeking the uncorrelated yet discriminative features. Benefited from this, the structure of data is kept in the Stiefel manifold, which avoids the potential trivial solution triggered by a conventional ridge regression model. Besides that, the uncorrelated constraint equips the model with the closed-form solution. In addition, we also incorporate a graph regularization term based on the principle of maximum entropy into the GURM model (URAFS), so as to embed the local geometric structure of data into the manifold learning. An efficient algorithm is designed to perform URAFS by virtue of the existing generalized powered iteration method. Extensive experiments on eight benchmark data sets among seven state-of-the-art methods on the task of clustering are conducted to verify the effectiveness and superiority of the proposed method.
Background: Technical efficiency, which is measured by calculating the ratio of products to resources, is the most important factor in assessing the efficiency status of organizations. Data ...envelopment analysis is useful to measure the efficiency score of all the units which have homogeneous input resources and output products and to rank them. The aim of this study was to measure and compare the efficiency of health performance in medical universities in Iran.Methods: The present research is a cross-sectional study to measure the efficiency of health performance using the national information of the health system of Iran. Input data include hospital beds, specialists, general physicians, dentists, pharmacists, nurses, midwives, computerised topography scan and magnetic resonance imagination devices, and Gini Index; also, the output data include pregnancy care coverage, infant mortality rate, low birth weight, and in-patient days. These data were attained from the annual Ministry of Health and Medical Education report in 2017 for 46 medical universities. To estimate the efficiency of health performance of each medical university using data envelopment analysis, we designed an input-oriented model with Variable Returns to Scale in GAMS 28.2.0. The effect of contextual factors on the efficiency score was calculated using the Tobit Regression model.Results: Results showed that only 19 (41%) medical universities were on the efficiency frontier. The highest mean of efficiency score was attributed to eastern areas, followed by the western and northern areas, and the worst status was related to southern parts of the country. The efficiency scores of universities located in northern areas were closer, while there was more difference among the efficiency scores of the universities of central areas of the country. Tobit regression shows that significant factors inefficiency include life expectancy and medical university class.Conclusion: The results of this study emphasized the differences in the performance efficiency of medical universities. Considering the inefficiency of smaller universities, we need to make careful decisions in establishing new universities in small cities.
•The negative binomial often seems to be more appropriate than other count models for informetric data.•Linear regression model predicted negative values for a few of the response variables ...modelled.•Linear regression model performed worse than both the negative binomial and lognormal models.•Overall, the negative binomial model performed slightly better than the lognormal model.
The purpose of the study is to compare the performance of count regression models to those of linear and lognormal regression models in modelling count response variables in informetric studies. Identified count response variables in informetric studies include the number of authors, the number of references, the number of views, the number of downloads, and the number of citations received by an article. Also of a count nature are the number of links from and to a website. Data were collected from the United States Patent and Trademark Office (www.uspto.gov), an open access journal (www.informationr.net/ir/), Web of Science, and Maclean's magazine. The datasets were then used to compare the performance of linear and lognormal regression models with those of Poisson, negative binomial, and generalized Poisson regression models. It was found that due to over-dispersion in most response variables, the negative binomial regression model often seems to be more appropriate for informetric datasets than the Poisson and generalized Poisson regression models. Also, the regression analyses showed that linear regression model predicted some negative values for five of the nine response variables modelled, and for all the response variables, it performed worse than both the negative binomial and lognormal regression models when either Akaike's Information Criterion (AIC) or Bayesian Information Criterion (BIC) was used as the measure of goodness of fit statistics. The negative binomial regression model performed significantly better than the lognormal regression model for four of the response variables while the lognormal regression model performed significantly better than the negative binomial regression model for two of the response variables but there was no significant difference in the performance of the two models for the remaining three response variables.
Chemicals may cause adverse effects on human health through binding to peroxisome proliferator-activated receptor γ (PPARγ). Hence, binding affinity is useful for evaluating chemicals with potential ...endocrine-disrupting effects. Quantitative structure–activity relationship (QSAR) regression models with defined applicability domains (ADs) are important to enable efficient screening of chemicals with PPARγ binding activity. However, lack of large data sets hindered the development of QSAR models. In this study, based on PPARγ binding affinity data sets curated from various sources, 30 QSAR models were developed using molecular fingerprints, two-dimensional descriptors, and five machine learning algorithms. Structure–activity landscapes (SALs) of the training compounds were described by network-like similarity graphs (NSGs). Based on the NSGs, local discontinuity scores were calculated and found to be positively correlated with the cross-validation absolute prediction errors of the models using the different training sets, descriptors, and algorithms. Moreover, innovative ADs were defined based on pairwise similarities between compounds and were found to outperform some conventional ADs. The curated data sets and developed regression models could be useful for evaluating PPARγ-involved adverse effects of chemicals. The SAL analysis and the innovative ADs could facilitate understanding of prediction results from QSAR models.
Industrial agglomeration (IA), a common industrial phenomenon, has been verified to have a significant impact on total factor productivity (TFP) in many industries. However, the impact of IA on TFP ...is seldom investigated in the construction industry, despite the existence of the industrial agglomeration phenomenon in the construction industry. As such, this study aims to probe into the impact of IA on TFP in the construction industry, so as to provide new insights into the industry development and improvement of TFP in the construction industry. Based on the competing results of the agglomeration effect and congestion effect caused by IA, this study proposed three hypotheses on the impact mechanism of IA on TFP in the construction industry. Then, the non-linear regression model and linear regression model were developed to test the hypotheses based on the provincial panel data from 2002 to 2017 in China. The empirical results reveal that IA has a positive linear impact on TFP in the construction industry, and the impact of IA on TFP in the Chinese construction industry during the observed period is in the embryonic stage. Besides, both the firm scale and economic development level have positive impacts on TFP, whereas the specialization structure has a negative impact. Hence, the government can encourage industrial agglomeration in the construction industry to enhance TFP, in order to leverage the knowledge spillovers, labor pool, and other benefits from IA.
The outlier issues in circular regression models have recently received much attention. The presence of outliers may cause the sign and magnitude of regression coefficients to vary, resulting in ...inaccurate model development and incorrect prediction. Many methods for detecting outliers in a circular regression model have been proposed in previous studies such as COVRATIO, D, M, A, and Chord statistics, but it is suspected that they are not very successful in the presence of multiple outliers in a data set since the masking and swamping is not considered in their studies. This study aimed to develop an outlier detection procedure using DFBETAc statistic for circular cases, where this new statistic will investigate and identify multiple outliers in the Jammalamadaka and Sarma circular regression model (JSCRM) by considering masking and swamping effect. Monte Carlo simulations are used to determine the corresponding cut-off point and the power of performance is investigated. The performance of the proposed statistic is evaluated by the proportion of detected outliers and the rate of masking and swamping. The simulation procedure is applied at 10% and 20% contamination levels for varying sample sizes. The results show that the proposed DFBETAcIS statistic for JSCRM successfully detect the outliers. For illustration purposes, this process is applied to wind direction data.
•A dynamic panel threshold regression model is proposed.•Renewable energy development can reduce energy intensity.•Renewable energy development has a threshold effect on energy intensity.•Effects are ...different between the high and the low regimes.
In recent years, renewable energy (RE) has seen rapid development worldwide. However, energy intensity has not declined at the same rate. This study applies a dynamic panel threshold regression model to explore the effects of RE development on energy intensity in 82 major countries. The results show that RE development has a significantly negative impact on energy intensity. Specifically, for every 10.0% increase in RE development, energy intensity reduces by 0.3%. The threshold point of the effect is determined and estimated at 2.3588 (the equivalent RE consumption is 10.58 billion MJ) between the low and the high development regimes of RE. Every 10.0% increase in the development of RE leads to a 2.2% decrease in energy intensity in the high development regime, but only 0.2% decrease in the low development regime. Economic development has a significantly negative impact on energy intensity, while energy consumption structure based on non-renewable energy creates has a significantly positive impact. All countries should increase the consumption of renewable energy to more than 10.58 billion MJ to cut energy intensity and improve the technical content of international trade products, especially in low renewable energy development level regimes.
