Parameters Of Regression Model Research Articles

Using Feasible Graphical Lasso Regression Method to Estimate the Parameters of General Linear Regression Model Under High Dimensional Data with Application

The high dimensions problem is one of the important problems that affect the data, and with company of high dimensional data in the general linear regression model, it becomes very difficult to use classical estimation methods such as maximum likelihood method and moments method because these methods lead to biased and inefficient estimates for parameters of the model. The necessity to use new methods to estimate the parameters of the general linear regression model. In this research, two methods were used, the Smooth Integration of Counting Absolute Deviation SICA method, as well as the Feasible Graphical Lasso method FGLasso. Simulation data were used, as well as real data representing the level of pollution in the Tigris River. The results compared using the Mean Square Error MSE and the results showed that the FGLasso estimator is the best

Testing the parametric form of the conditional variance in regressions based on distance covariance

A new test based on distance covariance is proposed for testing the parametric form of the conditional variance in parametric and nonparametric regression models. Inherit from the nice properties of distance covariance, the new test is very easy to implement in practice and less effected by the dimensionality of covariates. The asymptotic properties of the test statistic are investigated under the null and alternative hypotheses. The proposed test is consistent against any alternative hypothesis and can detect some classes of local alternative hypotheses converging to the null hypothesis at the parametric rate in both the parametric and nonparametric settings. As the limiting null distribution of the test statistic is intractable, a smooth residual bootstrap is proposed to approximate the limiting null distribution. Simulation studies are conducted to assess the finite sample performance of the proposed test. A real data set is also analyzed for illustration.

Estimation Of Parameter Regression Panel Data Model Using Least Square Dummy Variable Method

Panel data regression is a set of techniques for modeling the effect of independent variable on the dependent variable of panel data. The parameter estimation in the panel data regression model used the least squares method, but the difference between the intercept and the slope could not be known between time and between cross-section. One of the methods used is the Least Square Dummy Variable method (LSDV). The LSDV method is a method that has the same stages as the least squares method, but uses dummy variable to get different intercept score. This research uses the LSDV method to explain the differences in intercept between cross-sections using balanced panel data, namely the Human Development Index (HDI) data in South Sulawesi 2011-2017 to get fixed effect panel data regression model parameters on that data and the regencies with Average Length of School (ALS) and Life Expectancy (LE) variable that has the most influence on HDI based on the coefficient of determination criteria. According to the results of this research, the score of the coefficient of determination in the panel data regression model using the fixed effect model in each cross-section (regency), there are also three regencies with the highest coefficient of determination, respectively, Gowa, Pare-pare and Bantaeng regency that ALS and LE are able to explain the HDI variables 98.942%, 98.089% and 97.444%.

METODE REGRESI GULUD UNTUK MENGATASI MASALAH MULTIKOLINEARITAS PADA KASUS INDEKS KUALITAS LINGKUNGAN HIDUP DI INDONESIA TAHUN 2021

The environmental quality index is an indicator of environmental quality in Indonesia. EQI shows an increasing trend from 2018 to 2022, although Indonesia's environmental quality is ranked 116 out of 180 countries in the world. Therefore, the purpose of this study is to describe the characteristics of the EQI and the factors that can affect EQI, estimate the parameters of the ridge regression model and test the significance of the ridge regression model parameters to overcome the multicollinearity problem in the EQI case. This study uses 4 independent variables, namely population density, traffic, waste, and sanitation in Indonesia in 2021 as secondary data. In this study, ridge regression analysis is used because there is an almost linear relationship between the factors that are thought to affect the EQI. Ridge Regression is a technique that imposes limits (penalties) on parameter estimates in the regression model so that a reduction in the estimated coefficient value can overcome the multicollinearity problem. The results show that the optimal lambda value is 4.737078 when using the 5-fold cross-validation method. Variables that partially affect EQI are population density, waste, and environmental sanitation. The independent variables in this study that can explain the variability of EQI in Indonesia in 2021 is 59.52%, while 40.48% is explained by other variables that are not included in the model. Indonesia's EQI score in 2021 is 71.45 with the predicate "Good". Keywords: Environmental Quality Index, Ridge Regression, Cross Validation.

ANALISIS PENGARUH PEMBELAJARAN DARING TERHADAP PRESTASI BELAJAR SISWA DIMASA PANDEMI COVID-19 MENGGUNAKAN MODEL REGRESI BERGANDA (STUDI KASUS DI SMPN 33 BANJARMASIN)

The impact of the Covid-19 pandemic that occurred from 2019-2021 has had an impact on many sectors, one of which is the education sector. With this pandemic, learning takes place online which is possible for junior high school students is not an effective thing so that it has an impact on the final grades of student learning. This study aims to estimate the parameters of the multiple linear regression model formed in the analysis of the effect of online learning on student achievement at SMPN 33 Banjarmasin during the Covid-19 pandemic, and to explain the factors that significantly influence student achievement at SMPN 33 Banjarmasin during the pandemic. Covid-19. This study uses the independent variables of learning media and teacher teaching methods while the dependent variable uses student achievement. The population of this research is students at SMPN 33 Banjarmasin with a total sample of 120 respondents. Analysis was performed using multiple linear regression analysis. The results of the study showed that there was a significant influence between learning media and teacher teaching methods on student achievement at SMPN 33 Banjarmasin during the Covid-19 pandemic. Other results are shown based on testing the hypothesis that the variable that has the most influence is the teacher's teaching method variable. The teacher's teaching method variable (X2) has the largest relative contribution compared to the learning media variable (X1). Keywords: Online Learning, Learning Achievement, Multiple Linear Regression

Quantile Regression Based on the Weighted Approach with Dependent Truncated Data

This paper discusses the estimation of parameters in the quantile regression model for dependent truncated data. To account for the dependence between the survival time and the truncated time, the Archimedean copula model is used to construct the association. The parameters of the Archimedean copula model are estimated using certain existing approaches. An inference procedure based on a weighted approach is proposed, where the weights are set according to the variables of interest in the quantile regression model. The finite sample performance of the proposed approach is examined through simulations, and the method is applied to analyze two real datasets: the transfusion-related AIDS dataset and the retirement community center dataset.

Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support.

The condition of fetal overgrowth, also known as macrosomia, can cause serious health complications for both the mother and the infant. It is crucial to identify high-risk macrosomia-relevant pregnancies and intervene appropriately. Despite this need, there are several gaps in research related to macrosomia, including limited predictive models, insufficient machine learning applications, ineffective interventions, and inadequate understanding of how to integrate machine learning models into clinical decision-making. To address these gaps, we developed a machine learning-based model that uses maternal characteristics and medical history to predict macrosomia. Three different algorithms, namely logistic regression, support vector machine, and random forest, were used to develop the model. Based on the evaluation metrics, the logistic regression algorithm provided the best results among the three. The logistic regression algorithm was chosen as the final algorithm to predict macrosomia. The hyper parameters of the logistic regression model were tuned using cross-validation to achieve the best possible performance. Our results indicate that machine learning-based models have the potential to improve macrosomia prediction and enable appropriate interventions for high-risk pregnancies, leading to better health outcomes for both mother and fetus. By leveraging machine learning algorithms and addressing research gaps related to macrosomia, we can potentially reduce the health risks associated with this condition and make informed decisions about high-risk pregnancies.

Continued Infarction Growth and Penumbral Consumption After Reperfusion in Vaccine-Naïve Patients With COVID-19: A Case-Control Study.

Background: Neurologic sequelae of SARS-CoV-2 include potentially malignant cerebrovascular events arising from complex hemodynamic, hematologic, and inflammatory processes occurring in concert. Objective: This study concerns the hypothesis that, despite angiographic reperfusion, COVID-19 promotes continued consumption of at-risk tissue volumes after acute ischemic stroke (AIS) unlike what is seen with COVID-negative individuals, yielding critical insights into prognostication and monitoring paradigms in vaccine-naïve patients experiencing AIS. Methods: This retrospective study compared 100 consecutively presenting patients with COVID-19 and AIS between March 2020 and April 2021 with a contemporaneous cohort of 282 patients with AIS who did not have COVID-19. Reperfusion classes were dichotomized into positive (extended thrombolysis in cerebral ischemia [eTICI] score = 2c-3) and negative (eTICI score < 2c) groups. All patients underwent endovascular therapy following initial CT perfusion imaging (CTP) to document infarction core and total hypoperfusion volumes. Results: Ten COVID-positive (mean age ± SD, 67 ± 6 years; seven men, three women) and 144 COVID-negative patients (mean age, 71 ± 10 years; 76 men, 68 women) undergoing endovascular reperfusion with antecedent CTP and follow-up imaging comprised the final dataset. Initial infarction core and total hypoperfusion volumes were 1.5 ± 18 mL and 85 ± 100 mL in COVID-negative patients and 30.5 ± 34 mL and 117 ± 80.5 mL in COVID-positive patients, respectively. Final infarction volumes were significantly larger in patients with COVID-19, with median volumes of 77.8 mL versus 18.2 mL among control patients (p = .01), as were normalized measures of infarction growth relative to baseline infarction volume (p = .05). In adjusted logistic parametric regression models, COVID positivity emerged as a significant predictor for continued infarct growth (OR, 5.1 [95% CI, 1.0-25.95]; p = .05). Conclusion: These findings support the potentially aggressive clinical course of cerebrovascular events in patients with COVID-19, suggesting greater infarction growth and ongoing consumption of at-risk tissues, even following angiographic reperfusion. Clinical Impact: SARS-CoV-2 infection may promote continued infarction progression, despite angiographic reperfusion, in vaccine-naïve patients with large-vessel occlusion AIS. The findings carry potential implications for prognostication, treatment selection, and surveillance for infarction growth among revascularized patients in future waves of infection by novel viral strains.

James-Stein type estimators in beta regression model: simulation and application

Recently, the beta regression model has been used in several fields of science to model data in the form of rate or proportion. In this paper, some novel and improved methods to estimate parameters in the beta regression model are proposed. We consider a sub-space on the regression coefficients of the beta regression model and combine the unrestricted and restricted estimators then we present Stein-type and preliminary estimators. We develop the expressions for the proposed estimators' asymptotic biases and their quadratic risks. Numerical studies through Monte Carlo simulations are used to evaluate the performance of the proposed estimators in terms of their simulated relative efficiency. The results show that the proposed estimators outperform the unrestricted estimator when the restrictions hold. Finally, an empirical application is given to show how useful the proposed estimators are in the practical area.

The combined effects of coping and pain interference on army readiness.

Chronic pain and associated interference with daily activities are common in the military and impact Force readiness. Chronic pain affects one-third of service members and is a leading cause of medical non-readiness (MNR) in the military. Research suggests that underlying psychological mechanisms related to trait coping styles and pain interference (PI) affect functional outcomes, but little research exists examining this relationship within an Army population. The purpose of this study was to examine the combined effects of PI and coping on U.S. Army soldier readiness by using annual well-being data from the Global Assessment Tool (GAT) and medical non-readiness (MNR) based on duty restriction records. The sample comprised 866,379 soldiers who completed the GAT between 2014 and 2017 with no duty restrictions at the time of baseline GAT completion; subjects were observed through 2018 for duty restrictions. Parametric survival regression models with a Weibull distribution predicted demographic-adjusted hazards of MNR by dichotomized PI (no PI/PI) and beneficial/non-beneficial use of GAT coping components (good coping, bad coping, catastrophizing-flexibility, and catastrophizing-hopelessness). Incident MNR was evaluated for all duty restrictions, and stratified by selected body systems (upper extremity, lower extremity, psychiatric). Among soldiers with PI, hazards were higher in those reporting non-beneficial coping styles (bad coping, hopelessness) and lower in those reporting beneficial coping styles (good coping, flexibility). Across all coping styles, PI/coping interactions were particularly strong for catastrophizing-hopelessness and when examining MNR from psychiatric conditions. These findings suggest some synergistic associations between pain and coping that may impact pain-related occupational disability. Coping skills may be an effective interventional target for chronic pain reduction/prevention within military programs, such as the Master Resilience Training Course offered to soldiers in the Army. Further research should assess whether early coping style interventions can reduce pain-related outcomes.

Consistency of the LSE for Chirp Signal Parameters in the Models with Strongly and Weakly Dependent Noise

A time continuous statistical model of multiple chirp signal observed against the background of strongly-dependent stationary Gaussian noise is considered in the paper. Strong consistency of the least squares estimates for such a trigonometric regression model parameters is proved.

A data-driven soft-sensing approach using probabilistic latent variable model with functional data framework

Functional principal component analysis (FPCA) and functional partial least squares (FPLS) are two mainstream functional data analysis (FDA) methods, which have been commonly used to extract deep information hidden in the original data space. However, the process data always contain random noise, which affects the performance of FDA models. To overcome this issue, two functional probabilistic latent variable models (FPLVMs), including functional probabilistic principal component analysis (FPPCA) and functional probabilistic partial least squares (FPPLS) are proposed in this work. First, the process data are converted into functional data using the FDA. Subsequently, a log-likelihood function considering the noise factor and functional latent variables is designed. Finally, the regression model parameters are estimated using an expectation–maximisation algorithm. In contrast to FPPCA, FPPLS decomposes the process data and the key variable with constrained latent variables, which is similar to the partial least squares (PLS) and the principal component analysis (PCA). Moreover, the degeneration mechanism from FPLVMs into probabilistic latent variable models and latent variable models is discussed. An adaptive strategy with functional covariance is used to satisfy the online predictive capabilities of the model. Finally, the proposed approach is validated using a numerical case, the Tennessee Eastman process and an industrial o-xylene distillation column for evaluation.

Numerical simulation analysis of load-bearing capacity at joints of symmetrical cantilever-assembled bridges based on the logistic regression model

Abstract Bridge joint bearing capacity analysis is difficult to fit the requirements of the era of big data. The traditional way of bridge-bearing capacity numerical analysis exists in the wave of digital development of big data information, the Internet of Things, and artificial intelligence technology like a wild development. The traditional way of under the problem: is efficiency, engineering analysis is not accurate, data information paper, work responsibility is not in place, technical equipment, etc. This paper revolves around the numerical simulation analysis of the load-bearing capacity at the joints of symmetrical cantilever-assembled bridges based on the logistic regression model by finding literature to understand the concept and algorithm of the logistic regression model, construct logistic regression model based on dichotomous dependent variable logistic regression algorithm, logistic regression model parameter estimation, logistic regression. The three aspects of the model evaluation index are studied. The numerical simulation analysis of the load-bearing capacity at the symmetrical cantilever joint based on the logistic regression model, the shear strength calculation at the bridge joint, and the bridge longitudinal bar slip deformation are calculated, respectively, and then the actual value of the bridge load bearing capacity is derived. Simulation in the bridge bearing capacity at 0kN, 5kN, 10kN, 15kN, 20kN, 25kN, and 30kN, the model algorithm evaluation index under the three indicators of prediction rate, accuracy, deviation of the verification analysis. The results show that the prediction rate of the logistic regression model algorithm based on the load carrying capacity up to 30kN is 84%, the accuracy rate is 94%, and the deviation is 11%, and the three index values have better performance compared with the one-dimensional linear regression model algorithm and the multiple linear regression model algorithms. This study is beneficial to extend the bridge structural analysis function and make the bridge load capacity assessment more automatic; it can facilitate the comparison of historical data of bridge tests, longitudinal change of indicators at different testing time points, and grasp the current situation and change trend of bridges. Thus it is of great historical significance to the development of the Chinese bridge infrastructure industry.

Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2

Lakes and ponds are extensive features throughout the circumpolar region, spanning a broad range of environmental conditions which controls their hydro-ecological processes and spatiotemporal distribution. The physical characteristics of these freshwater ecosystems, including their extent and depth, are particularly responsive to climatic conditions. Thus, having the ability to efficiently map and monitor these elements is crucial as the climate continues to warm, especially over large spatial extents. In this study, satellite derived bathymetry (SDB) methods were implemented to model regional inland Arctic water depths within the open-source and cloud-based Google Earth Engine (GEE) platform. High-resolution (10 m) spectral reflectance data from Sentinel-2 was used as covariates in non-parametric and non-linear machine learning (ML) models, namely random forest (RF), support vector regression (SVR), and classification and regression trees (CART). These three ML models were also compared to a more classical and parametric multiple linear regression (MLR) model. All models were calibrated using in situ bathymetric data collected near the Toolik Field Station, in the Alaskan Arctic tundra. With such a large sample repository available, the effects of training set size, noise, and multicollinearity on model performance was comprehensively investigated. Results clearly demonstrated the superior efficacy and robustness of the RF algorithm for predicting water depths, achieving a best coefficient of determination (R2) of 0.74, mean absolute error (MAE) of 2.12 m, and root mean square error (RMSE) of 3.04 m. Overall, this study highlights the potential of advanced and flexible ML algorithms within the GEE, and demonstrates the capabilities of the polar-orbiting Copernicus Sentinel-2 satellite for this Arctic application. In the future, this ML-driven methodology can be applied using GEE’s cloud infrastructure to produce updated, cost-effective, and accurate bathymetry maps over large expanses of the Arctic tundra.

Time-dependent shrinkage of time-varying parameter regression models

This article studies the time-varying parameter (TVP) regression model in which the regression coefficients are random walk latent states with time-dependent conditional variances. This TVP model is flexible to accommodate a wide variety of time variation patterns but requires effective shrinkage on the state variances to avoid over-fitting. A Bayesian shrinkage prior is proposed based on reparameterization that translates the variance shrinkage problem into a variable shrinkage one in a conditionally linear regression with fixed coefficients. The proposed prior allows strong shrinkage for the state variances while maintaining the flexibility to accommodate local signals. A Bayesian estimation method is developed that employs the ancilarity-sufficiency interweaving strategy to boost sampling efficiency. Simulation study and an empirical application to forecast inflation rate illustrate the benefits of the proposed approach.

A marginal structural model to estimate the effect of antidepressant medication treatment on major cardiovascular events among people with post-traumatic stress disorder.

Previous evidence on antidepressant medication and cardiovascular disease (CVD) among patients with posttraumatic stress disorder (PTSD) has been inconclusive. We estimated the association between antidepressant medication and CVD by applying a marginal structural model. We analyzed medical utilization records of 27 170 people with PTSD without prior major cardiovascular events in the Korean National Health Insurance Database (NHID). PTSD and CVD were defined in accordance with the recorded ICD-10 diagnostic codes. We acquired information on antidepressant use from the NHID and categorized them by medication type. A composite major adverse cardiovascular events (MACE) outcome was defined as coronary artery disease with revascularization, ischaemic stroke, and/or haemorrhagic stroke. We used inverse probability of treatment weighting to estimate the parameters of a marginal structural discrete-time survival analysis regression model, comparing the resulting estimates to those derived from traditional time-fixed and time-varying Cox proportional hazards regression. We calculated cumulative daily defined doses to test for a dose-response relationship. People exposed to antidepressants showed a higher hazard of MACE [hazard ratio (HR) 1.34, 95% confidence interval (CI) 1.18-1.53]. The estimated effects were strongest for selective serotonin reuptake inhibitors (HR 1.24, 95% CI 1.08-1.44) and TCAs (HR 1.33, 95% CI 1.13-1.56). Exposure to serotonin-norepinephrine reuptake inhibitors did not appear to increase the risk of MACE. People exposed to higher doses of antidepressants showed higher risk of MACE. In a national cohort of people with PTSD, exposure to antidepressant medications increased the risk of MACE in a dose-response fashion.

Dynamic weighing algorithm for dairy cows based on time domain features and error compensation

Weight is an essential indicator for the growth and development of cows. Traditional static weighing method requires a lot of labor and financial resources, the existing dynamic weighing algorithm has no consideration of the influence of hidden information on weighing results during cows' movement, and fails to meet the weighing requirements in complex environments. In this paper, we propose a cow dynamic weighing algorithm based on time domain features and error compensation, quantitatively analyze the hidden information in the dynamic weighing signal, and compensate for the error of the preliminary prediction results, leading to a high-precision dynamic weighing of cows. Firstly, the dynamic weighing signal of cows is pre-processed to obtain the effective signal, and each time domain feature of the effective signal are extracted. Then, the effective signal is decomposed by using the empirical wavelet transform (EWT), and the trend component's mean value is taken as the prediction value of EWT, so that the error between the prediction weight value and the real weight can be calculated. Next, four time-domain features are used as input and the error values are used as output to construct the data set, and the sparrow search algorithm (SSA) is used to obtain the optimal parameters of support vector regression (SVR) model for the training. Finally, the error prediction values output from the model are summed with the prediction values of EWT as the final weight prediction value. To evaluate the performance of the method in this paper, we collect dynamic weighing data from 626 cows, and then randomly select 501 data as the training set while the remaining 125 data as the test set. The average relative error of weight prediction of our method is 0.68%, and the root mean square error is 4.83 kg, better than the existing dynamic weighing algorithms. The experimental results show that the method in this paper is feasible to quantitatively analyze the implicit information of the effective signal according to the time domain features, and the error compensation method can effectively reduce dynamic weighing errors, particularly under extreme conditions, making the method more robust and better meeting the practical needs of farms.

The Energy Saving Renovation of Regional Buildings Based on Bayesian Estimation

With China's new urbanization vision and existing urban renewal and building energy efficiency improvement project, large-scale energy conservation renovation of regional buildings has become an important content of ecological environmental protection and low-carbon and an important means to improve the regional energy efficiency level. Of energy-saving reform foundation database is presented in this paper, including construction, power consumption, equipment, operation, technology and willingness to word stock, statistics obtained the regional energy complex parameter (envelope thermal performance, elevator equipment, power and efficiency of cooling and heat sources, etc.) statistical rule and can use the benchmark, build the regional buildings energy consumption prediction model based on bayesian estimation, The probability matrix of large-scale reconstruction of regional buildings is obtained. At the same time, the global sensitive parameter analysis method and the single parameter regression model of energy saving transformation factor are established, and it is found that the internal load factor has the most significant effect on energy consumption. By adjusting energy parameters, the probability distribution curve of energy saving and the distribution of normalized value under the constraint conditions of different reconstruction scenarios were simulated, and the discrete MC simulation method was used to predict the regional overall energy saving reconstruction based on renovation intention, which could realize the trade-off judgment and gradual optimization of the strategy package of energy saving measures combination.

Tobacco Patterns and Risk of Chronic Obstructive Pulmonary Disease: Results From a Cross-Sectional Study

IntroductionThere is still uncertainty about which aspects of cigarette smoking influence the risk of Chronic Obstructive Pulmonary Disease (COPD). The aim of this study was to estimate the COPD risk as related to duration of use, intensity of use, lifetime tobacco consumption, age of smoking initiation and years of abstinence. MethodsWe conducted an analytical cross-sectional study based on data from the EPISCAN-II study (n=9092). All participants underwent a face-to-face interview and post-bronchodilator spirometry was performed. COPD was defined as post-bronchodilator FEV1/FVC<70%. Parametric and nonparametric logistic regression models with generalized additive models were used. Results8819 persons were included; 858 with COPD and 7961 without COPD. The COPD risk increased with smoking duration up to ≥50 years [OR 3.5 (95% CI: 2.3–5.4)], with smoking intensity up to ≥39cig/day [OR 10.1 (95% CI: 5.3–18.4)] and with lifetime tobacco consumption up to >29 pack-years [OR 3.8 (95% CI: 3.1–4.8)]. The COPD risk for those who started smoking at 22 or later was 0.9 (95% CI: 0.6–1.4). The risk of COPD decreased with increasing years of cessation. In comparison with both never smokers and current smokers, the lowest risk of COPD was found after 15–25 years of abstinence. ConclusionCOPD risk increases with duration, intensity, and lifetime tobacco consumption and decreases importantly with years of abstinence. Age at smoking initiation shows no effect. After 15–25 years of cessation, COPD risk could be equal to that of a never smoker. This work suggests that the time it takes to develop COPD in a smoker is about 30 years.

Reference values for carotid artery intima-media thickness among community adult dwellers in Shenzhen City

Objective: To establish reference values for carotid intima-media thickness (CIMT) of adult dwellers in Shenzhen City. Methods: The study was conducted based on the Shenzhen heart failure epidemiological survey from 2021 to 2022. In this survey, residents aged 18 years and above in Shenzhen were selected by using a multi-stage stratified random sampling method. General information, cardiovascular disease (CVD) related behavior and carotid ultrasound examination and etc. were collected from the participants. People with CVD factors, a history of atherosclerotic cardiovascular disease, carotid plaque or having no carotid ultrasound examination results were excluded. The parameter regression model based on fractional polynomial was used to establish the reference values of CIMT by age and sex. Results: A total of 2 163 healthy individuals were enrolled in the final analysis, including 576 males (26.6%) and 1 587 females (73.4%). The fractional polynomial regression of the CIMT mean and standard deviation was obtained. For men, the regression was meanCIMT=0.324 7+0.006 9×age and SDCIMT=0.076 9+0.001 2×age. For women, the regression was meanCIMT=0.354 9+0.005 4×age and SDCIMT=0.041 6+0.002 0×age. Conclusion: The age and sex reference values for CIMT of adult people in Shenzhen established in this study could provide the latest reference standards for early screening of subclinical CVD.