Lasso回归：从解释到预测

Zhang, Lijin; Wei, Xiayan; Lu, Jiaqi; Pan, Junhao

doi:10.3724/sp.j.1042.2020.01777

Cited by 22 publications

(13 citation statements)

References 75 publications

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“…As the regularization term of punishment, the l 1 norm has a stronger capacity to sparse regression coefficient vector and can compress the estimated coefficient of the redundant predictor variable to 0, which plays the role of variable screening at the same time as the compression coefficient. Therefore, it can effectively avoid the problem of insufficient generalization ability of the model caused by overfitting and obtain a simpler model with higher prediction efficiency [ 48 ]. Compared with ridge regression, lasso regression has a stronger function of variable selection and has the function of dimension reduction in high-dimensional space.…”

Section: Methodsmentioning

confidence: 99%

Can health service equity alleviate the health expenditure poverty of Chinese patients? Evidence from the CFPS and China health statistics yearbook

Tang

Yao

et al. 2021

BMC Health Serv Res

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Objectives To comprehend the relationship between various indicators of health service equity and patients’ health expenditure poverty in different regions of China, identify areas where equity in health service is lacking and provide ideas for improving patients’ health expenditure poverty. Method Data from China Family Panel Studies (CFPS) in 2018 and the HFGT index formula were used to calculate the health expenditure poverty index of each province. Moreover, Global Moran’s I and Local Moran’s I test are applied to measure whether there is spatial aggregation of health expenditure poverty. Finally, an elastic net regression model is established to analyze the impact of health service equity on health expenditure poverty, with the breadth of health expenditure poverty as the dependent variable and health service equity as the independent variable. Results In the developed eastern provinces of China, the breadth of health expenditure poverty is relatively low. There is a significant positive spatial agglomeration. “Primary medical and health institutions per 1,000 population”, “rural doctors and health workers per 1,000 population”, “beds in primary medical institutions per 1,000 population”, “proportion of government health expenditure” and “number of times to participate in medical insurance (be aided) per 1,000 population” have a positive impact on health expenditure poverty. “Number of health examinations per capita” and “total health expenditure per capita” have a negative impact on health expenditure poverty. Both effects passed the significance test. Conclusion To enhance the fairness of health resource allocation in China and to alleviate health expenditure poverty, China should rationally plan the allocation of health resources at the grassroots level, strengthen the implementation of hierarchical diagnosis and treatment and encourage the investment in business medical insurance industry. Meanwhile, it is necessary to increase the intensity of medical assistance and enrich financing methods. All medical expenses of the poorest should be covered by the government.

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Section: Methodsmentioning

confidence: 99%

Can health service equity alleviate the health expenditure poverty of Chinese patients? Evidence from the CFPS and China health statistics yearbook

Tang

Yao

et al. 2021

BMC Health Serv Res

View full text Add to dashboard Cite

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“…Based on the loss function of the least squares (ordinary least square (OLS)), Lasso uses the sum of the absolute values of the regression coefficients as a penalty function to compress the regression coefficients. When the sum of the absolute values of the regression coefficients is small enough, some regression coefficients can be compressed to zero, and then the variables with zero coefficients can be eliminated, so as to achieve the effect of variable selection [9]. Assumed linear regression model X 0 � βX + ε, where X 0 is the system behavior characteristic vector, X is the influence factor variable matrix, and β is the coefficient vector, then the coefficient estimation of Lasso method is as follows:…”

Section: Pso-gm (1 N) Modelmentioning

confidence: 99%

Prediction of Vegetable Supply in Henan Province Based on PSO-GM (1, N) Model

Guo

2021

Journal of Mathematics

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GM (1, N) model is one of the grey prediction models considering the influence of many factors. This paper improves GM (1, N) model and constructs PSO-GM (1, N) model. Firstly, Lasso method is used to select the influencing factors, then the priority of influencing factors and the value of parameter N in GM (1, N) model are determined, and finally PSO method is used to optimize GM (1, N) model. Taking the vegetable supply in Henan Province as the research object, this paper makes an empirical test by using PSO-GM (1, N) model. The results show that the key factors affecting the vegetable supply in Henan Province are the number of rural employees, highway mileage, and application of pesticide. The vegetable supply in Henan Province will continue to show a steady growth trend in the next three years.

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“…The proposed method provides the most accurate and unbiased estimation by the sum of the minimum residuals (30), but it also has some shortcomings, such as overfitting results and poor predictions on future observations (31). In fact, those problems are even more serious when there are many predictors in a regression model (29,32,33).…”

Section: Introductionmentioning

confidence: 99%

“…Supervised learning is divided into classification and regression (34). Lasso regression (32,38) used in this study is a kind of regression that used least angle regression algorithm instead of least squares.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed method can shrink the small coefficient toward zero by adding a penalty term in the process of model estimation (40). Consequently, it can obtain higher model prediction accuracy and model generalizability (32). Moreover, Lasso regression can help researchers to perform variable selection and help them to get more concise and more efficient models (32,41).…”

Section: Introductionmentioning

confidence: 99%

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The Effect of College Students' Adaptability on Nomophobia: Based on Lasso Regression

Luo

Ren

et al. 2021

Front. Psychiatry

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Smartphones can improve our lives, but also consume our lives. It is known that problematic mobile phone use, such as nomophobia, can lead to some mental health problems. So far, psychological factors behind nomophobia were yet to be fully discovered. Previous studies showed that individuals' adaptability was closely related to nomophobia. However, adaptability was a complex construct that contains various components, and it was unclear whether these components contributed equally to nomophobia. This study investigated 678 college students by using Chinese versions of the nomophobia questionnaire, mobile phone addiction tendency scale, and freshmen adaptability scale. Lasso regression was used to further explore the key factors that could affect nomophobia. Model results showed that the value of λ+1se was [0.303, 0.423] at the minimum mean squared error in the training data. Emotional adaptability significantly predicted the fear of being unable to access information (β = −0.022, p < 0.001), losing convenience (β = −0.067, p < 0.001), and losing Internet connection (β = −0.003, p < 0.01) after λ+1se was included in the testing data, and the R2 were 0.496, 0.483, and 0.493. Homesickness adaptability significantly predicted the fear of losing contact (β = −0.056, p < 0.05), and R2 was 0.508. In addition, similar results were obtained by using datasets of mobile phone addiction and adaptability. Therefore, we concluded that the emotional adaptability has an important effect on nomophobia. Additionally, we also found that homesickness adaptability has an important role in predicting fear of losing contact.

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Lasso回归：从解释到预测

Cited by 22 publications

References 75 publications

Can health service equity alleviate the health expenditure poverty of Chinese patients? Evidence from the CFPS and China health statistics yearbook

Can health service equity alleviate the health expenditure poverty of Chinese patients? Evidence from the CFPS and China health statistics yearbook

Prediction of Vegetable Supply in Henan Province Based on PSO-GM (1, N) Model

The Effect of College Students' Adaptability on Nomophobia: Based on Lasso Regression

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