Prediction of Rural Residents’ Consumption Expenditure Based on Lasso and Adaptive Lasso Methods

When the variable of model is large, the Lasso method and the Adaptive Lasso method can effectively select variables. This paper prediction the rural residents’ consumption expenditure in China, based on respectively using the Lasso method and the Adaptive Lasso method. The results showed that both can effectively and accurately choose the appropriate variable, but the Adaptive Lasso method is better than the Lasso method in prediction accuracy and prediction error. It shows that in variable selection and parameter estimation, Adaptive Lasso method is better than the Lasso method.

Bayesian Regularized Quantile Regression Analysis Based on Asymmetric Laplace Distribution

In recent years, variable selection based on penalty likelihood methods has aroused great concern. Based on the Gibbs sampling algorithm of asymmetric Laplace distribution, this paper considers the quantile regression with adaptive Lasso and Lasso penalty from a Bayesian point of view. Under the non-Bayesian and Bayesian framework, several regularization quantile regression methods are systematically compared for error terms with different distributions and heteroscedasticity. Under the error term of asymmetric Laplace distribution, statistical simulation results show that the Bayesian regularized quantile regression is superior to other distributions in all quantiles. And based on the asymmetric Laplace distribution, the Bayesian regularized quantile regression approach performs better than the non-Bayesian approach in parameter estimation and prediction. Through real data analyses, we also confirm the above conclusions.

Comparison of Several Data Mining Methods in Credit Card Default Prediction

LightGBM is an open-source, distributed and high-performance GB framework built by Microsoft company. LightGBM has some advantages such as fast learning speed, high parallelism efficiency and high-volume data, and so on. Based on the open data set of credit card in Taiwan, five data mining methods, Logistic regression, SVM, neural network, Xgboost and LightGBM, are compared in this paper. The results show that the AUC, F1-Score and the predictive correct ratio of LightGBM are the best, and that of Xgboost is second. It indicates that LightGBM or Xgboost has a good performance in the prediction of categorical response variables and has a good application value in the big data era.

职场负面八卦会抑制员工越轨创新行为吗?一个被调节的链式中介模型

越轨创新作为组织内的一种创新行为,对提升组织创新绩效和整体利益具有重要作用。尽管已有研究探究了越轨创新行为的激活因素,但对其抑制因素的关注相对不足。以认知-情感人格系统理论为整体研究逻辑框架,以资源保存理论解释变量间的内部机理,构建职场负面八卦通过心理安全感与和谐式工作激情作用于越轨创新行为的链式中介模型,并探讨核心自我评价在链式中介模型中的边界作用。通过对7家科技型企业的多时点下属-主管配对数据进行了分析,研究结果表明:职场负面八卦对越轨创新行为具有负向影响;心理安全感与和谐式工作激情在职场负面八卦对员工越轨创新行为的负向影响过程中起链式中介作用;核心自我评价通过缓和职场负面八卦对员工心理安全感的负向影响,进而调节心理安全感与和谐式工作激情的链式中介作用。

Pathogenesis of premature coronary artery disease:Focus on risk factors and genetic variants

The development of premature coronary artery disease(PCAD)is dependent on both genetic predisposition and traditional risk factors.Strategies for unraveling the genetic basis of PCAD have evolved with the advent of modern technologies.Genome-wide association studies(GWASs)have identified a considerable number of common genetic variants that are associated with PCAD.Most of these genetic variants are attributable to lipid and blood pressure-related single-nucleotide polymorphisms(SNPs).The genetic variants that predispose individuals to developing PCAD may depend on race and ethnicity.Some characteristic genetic variants have been identified in Chinese populations.Although translating this genetic knowledge into clinical applications is still challenging,these genetic variants can be used for CAD phenotype identification,genetic prediction and therapy.In this article we will provide a comprehensive review of genetic variants detected by GWASs that are predicted to contribute to the development of PCAD.We will highlight recent findings regarding CAD-related genetic variants in Chinese populations and discuss the potential clinical utility of genetic variants for preventing and managing PCAD.

Bioinformatics analysis of SARS-CoV-2 infectionassociated immune injury and therapeutic prediction for COVID-19

Background:Severe acute respiratory syndrome coronavirus 2 is a highly contagious viral infection,without any available targeted therapies.The high mortality rate of COVID-19 is speculated to be related to immune damage.Methods:In this study,clinical bioinformatics analysis was conducted on transcriptome data of coronavirus infection.Results:Bioinformatics analysis revealed that the complex immune injury induced by coronavirus infection provoked dysfunction of numerous immune-related molecules and signaling pathways,including immune cells and toll-like receptor cascades.Production of numerous cytokines through the Th17 signaling pathway led to elevation in plasma levels of cytokines(including IL6,NF-kB,and TNF-a)followed by concurrent inflammatory storm,which mediates the autoimmune response.Several novel medications seemed to display therapeutic effects on immune damage associated with coronavirus infection.Conclusions:This study provided insights for further large-scale studies on the target therapy on reconciliation of immunological damage associated with COVID-19.