An Adaptive Lasso Grey Model for Regional FDI Statistics Prediction
Source abstract
Abstract
To overcome the deficiency of traditional mathematical statistics methods, an adaptive Lasso grey model algorithm for regional FDI (foreign direct investment) prediction is proposed in this paper, and its validity is analyzed. Firstly, the characteristics of the FDI data in six provinces of Central China are generalized, and the mixture model's constituent variables of the Lasso grey problem as well as the grey model are defined. Next, based on the influencing factors of regional FDI statistics (mean values of regional FDI and median values of regional FDI), an adaptive Lasso grey model algorithm for regional FDI was established. Then, an application test in Central China is taken as a case study to illustrate the feasibility of the adaptive Lasso grey model algorithm in regional FDI prediction. We also select RMSE (root mean square error) and MAE (mean absolute error) to demonstrate the convergence and the validity of the algorithm. Finally, we train this proposedal gorithm according to the regional FDI statistical data in six provinces in Central China from 2006 to 2018. We then use it to predict the regional FDI statistical data from 2019 to 2023 and show its changing tendency. The extended work for the adaptive Lasso grey model algorithm and its procedure to other regional economic fields is also discussed.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Grey System Theory Applications · Energy, Environment, Economic Growth
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (32)
Show source-provided references
1. The formation of the Eurasian economic union: How successful is the Russian regional hegemony10.1016/j.euras.2015.06.002 ↗
2. On the application of interval PCA to process monitoring: A robust strategy for sensor FDI with new efficient control statistics10.1016/j.jprocont.2018.01.006 ↗
3. The role of fiscal and monetary policies in the Brazilian economy: Understanding recent institutional reforms and economic changes10.1016/j.qref.2016.07.005 ↗
4. Dataanalysisof MS-based clinical lipidomics studies with crossover design: A tutorial mini-review of statistical methods10.1016/j.clinms.2019.05.002 ↗
5. Factors associated with discontinuation in the drug and placebo groups of trials of second-generation antipsychotics for acute schizophrenia: A meta-regression analysis: Discontinuation in antipsychotic trials10.1016/j.jpsychires.2020.08.003 ↗
6. Quantile regression and variable selection for partially linear single-index models with missing censoring indicators10.1016/j.jspi.2019.04.008 ↗
7. SpencerLQD-RKHS-based distribution-to-distribution regression methodology for restoring theprobabilitydistributionsof missing SHM data10.1016/j.ymssp.2018.11.052 ↗
8. Comprehensive study of feature selection methods to solve multicollinearity problem according to evaluation criteria10.1016/j.eswa.2017.01.048 ↗
9. Estimation of covariance matrix via the sparse Cholesky factor with lasso10.1016/j.jspi.2010.04.048 ↗
10. Unbiased adaptive lasso parameter estimation for diffusion processes10.1016/j.ifacol.2018.09.144 ↗
11. Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator10.1016/j.energy.2018.03.045 ↗
12. A novel car-following inertiagraymodeland its application in forecasting short-term traffic flow10.1016/j.apm.2020.06.020 ↗
13. An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction10.1016/j.physa.2017.05.044 ↗
14. Coverless steganography based on image retrieval of DenseNet features and DWT sequence mapping10.1016/j.knosys.2019.105375 ↗
15. Coverless image steganography based on multi-object recognition10.1109/TCSVT.2020.3033945 ↗
16. Coverless image steganography based on generative adversarial network10.3390/math8091394 ↗
17. Searchable encrypted image retrieval based on multi-feature adaptive late-fusion10.3390/math8061019 ↗
18. A privacy-preserving and traitor tracking content-based image retrieval scheme in cloud computing10.1007/s00530-020-00734-w ↗
19. A biological image classification method based on improved CNN10.1016/j.ecoinf.2020.101093 ↗
20. News text topic clustering optimized method based on IF-IDF algorithm on spark10.32604/cmc.2020.06431 ↗
21. An adjust duty cycle method for optimized congestion avoidance and reducing delay for wsns10.32604/cmc.2020.011458 ↗
22. Regression shrinkage and selection via the lasso10.1111/j.2517-6161.1996.tb02080.x ↗
23. Efron · Least Angle Regression · 1999
24. The adaptive lasso and its oracle properties10.1198/016214506000000735 ↗
25. Study on chaotic characteristics of GM (1,1) model in grey system
26. Z.Chen · Systems Engineering · 1988 · 8 · 67
27. Analysis and evaluation of grey prediction model
28. Direct modeling method of unbiased grey GM (1,1) model
29. Analysis of chaotic characteristics of unbiased GM (1,1) model
30. Image super-resolution reconstruction based on feature map attention mechanism10.1007/s10489-020-02116-1 ↗
31. The image annotation algorithm using convolutional features from intermediate layer of deep learning10.1007/s11042-020-09887-2 ↗
32. Comprehensive evaluation of industrial carrying capacity in Central China