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Bayesian Estimation and model selection for the Spatiotemporal Autoregressive model with Autoregressive Conditional Heteroscedasticity Errors

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应用数学学报:英文版 2023年第4期39卷 972-989页

作者： Bing SU Fu-kang ZHU Ju HUANG Department of Statistics&Actuarial Science University of Hong KongHong Kong 999077China School of Mathematics Jilin UniversityChangchun 130012China National Energy Administration Information Center Beijing 100045China

The spatial and spatiotemporal autoregressive conditional heteroscedasticity(STARCH) models receive increasing attention. In this paper, we introduce a spatiotemporal autoregressive(STAR) model with STARCH errors, which can capture the spatiotemporal dependence in mean and variance simultaneously. The Bayesian estimation and model selection are considered for our model. By Monte Carlo simulations, it is shown that the Bayesian estimator performs better than the corresponding maximum-likelihood estimator, and the Bayesian model selection can select out the true model in most times. Finally, two empirical examples are given to illustrate the superiority of our models in fitting those data.

关键词： autoregressive conditional heteroscedasticity model Bayesian estimation model selection spatial ARCH model spatial panel model spatiotemporal model

A general evaluation system for optimal selection performance of radar clutter model

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Journal of Systems Engineering and Electronics 2023年第6期34卷 1520-1525页

作者： YANG Wei ZHANG Liang YANG Liru ZHANG Wenpeng SHEN Qinmu College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China

The optimal selection of radar clutter model is the premise of target detection,tracking,recognition,and cognitive waveform design in clutter background.Clutter characterization models are usually derived by mathematical simplification or empirical data fitting.However,the lack of standard model labels is a challenge in the optimal selection process.To solve this problem,a general three-level evaluation system for the model selection performance is proposed,including model selection accuracy index based on simulation data,fit goodness indexs based on the optimally selected model,and evaluation index based on the supporting performance to its third-party.The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways,and can be popularized and applied to the evaluation of other similar characterization model selection.

关键词： radar clutter clutter characterization model model selection performance evaluation.

model selection METHOD BASED ON MAXIMAL INFORMATION COEFFICIENT OF RESIDUALS

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Acta Mathematica Scientia 2014年第2期34卷 579-592页

作者：谭秋衡蒋杭进丁义明 Wuhan Institute of Physics and Mathematics CASWuhan 430071China University of CAS Key Laboratory of Magnetic Resonance in Biological Systems Wuhan Institute of Physics and MathematicsCAS National Center for Mathematics and Interdisciplinary Sciences CAS

The traditional model selection criterions try to make a balance between fitted error and model complexity. Assumptions on the distribution of the response or the noise, which may be misspecified, should be made before using the traditional ones. In this ar- ticle, we give a new model selection criterion, based on the assumption that noise term in the model is independent with explanatory variables, of minimizing the association strength between regression residuals and the response, with fewer assumptions. Maximal Information Coe^cient （MIC）, a recently proposed dependence measure, captures a wide range of associ- ations, and gives almost the same score to different type of relationships with equal noise, so MIC is used to measure the association strength. Furthermore, partial maximal information coefficient （PMIC） is introduced to capture the association between two variables removing a third controlling random variable. In addition, the definition of general partial relationship is given.

关键词： model selection residual maximal information coefficient partial maximalinformation coefficient

User Purchase Intention Prediction Based on Improved Deep Forest

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Computer modeling in Engineering & Sciences 2024年第4期139卷 661-677页

作者： Yifan Zhang Qiancheng Yu Lisi Zhang School of Computer Science and Engineering North Minzu UniversityYinchuan750030China Laboratory of Graphics and Images of the State Ethnic Affairs Commission North Minzu UniversityYinchuan750030China

Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection.To address this issue,based on the deep forest algorithm and further integrating evolutionary ensemble learning methods,this paper proposes a novel Deep Adaptive Evolutionary Ensemble(DAEE)model.This model introduces model diversity into the cascade layer,allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns.Moreover,this paper optimizes the methods of obtaining feature vectors,enhancement vectors,and prediction results within the deep forest algorithm to enhance the model’s predictive accuracy.Results demonstrate that the improved deep forest model not only possesses higher robustness but also shows an increase of 5.02%in AUC value compared to the baseline model.Furthermore,its training runtime speed is 6 times faster than that of deep models,and compared to other improved models,its accuracy has been enhanced by 0.9%.

关键词： Purchase prediction deep forest differential evolution algorithm evolutionary ensemble learning model selection

model selection in Estimation of Covariance Functions for Growth of Angora Goats

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Agricultural Sciences in China 2010年第7期9卷 1041-1049页

作者： LIU Wen-zhong ZHANG Yuan ZHOU Zhong-xiao College of Animal Science and Technology Shanxi Agricultural University Taigu 030801 P.R.China College of Animal Science and Technology China Agricultural University Beijing 100193 P.R.China

Covariance functions have been proposed as an alternative to model longitudinal data in animal breeding because of their various merits in comparison to the classical analytical methods.In practical estimation,different models and polynomial orders fitted can influence the estimates of covariance functions and thus genetic parameters.The objective of this study was to select model for estimation of covariance functions for body weights of Angora goats at 7 time points.Covariance functions were estimated by fitting 6 random regression models with birth year,birth month,sex,age of dam,birth type,and relative birth date as fixed effects.Random effects involved were direct and maternal additive genetic,and animal and maternal permanent environmental effects with different orders of fit.selection of model and orders of fit were carried out by likelihood ratio test and 4 types of information criteria.The results showed that model with 6 orders of polynomial fit for direct additive genetic and animal permanent environmental effects and 4 and 5 orders for maternal genetic and permanent environmental effects,respectively,were preferable for estimation of covariance functions.models with and without maternal effects influenced the estimates of covariance functions greatly.Maternal permanent environmental effect does not explain the variation of all permanent environments,well suggesting different sources of permanent environmental effects also has large influence on covariance function estimates.

关键词： Angora goats growth covariance function model selection random regression model

model selection FOR CANONICAL CORRELATION ANALYSIS

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Acta Mathematicae Applicatae Sinica 2001年第4期17卷 539-549页

作者：赵林城彭利民吴耀华 Department of Statistics and Finance University of Science and Technology of China Hefei China

In this paper, we study a general criterion for estimating the rank of canonical correlation matrix (CCM). Besides the strong consistency, we give the exponential-type bounds under certain conditions on the probability of wrong detection of the rank of CCM. On the basis of this criterion, we give two methods to determine the multiplicities of canonical correlation coefficents. And the strong consistency of them is established.

关键词： Canonical correlation model selection consistency Akaike's information criterion

model selection in Spatial Autoregressive models with Varying Coefficients

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Frontiers of Economics in China-Selected Publications from Chinese Universities 2018年第4期13卷 559-576页

作者： Hongjie Wei Yan Sun Meidi Hu School of Economics Shanghai University of Finance and EconomicsShanghai 200433China

Spatial autoregressive (SAR) models with varying coefficients are useful for capturing heterogeneous effects of the impacts of covariates as well as spatial interaction in empirical studies, and a wide range of popular models can be seen as its special cases, such as linear SAR models. In this study, we will propose a unified model selection method for the SAR model with varying coefficients to achieve two targets simultaneously: (1) variable selection (eliminate irrelevant covariates), and (2) identification of the covariates with constant effect among the relevant covariates. To do so, we follow the idea of group LASSO to incorporate two penalty functions to simultaneously do model selection and estimation. Monte Carlo experiments show that the proposed method performs well in finite samples. Finally, we illustrate the method with an application to the housing data of Chinese cities.

关键词： Adaptive group LASSO Heterogeneous effects model selection Spatial dependence Varying coefficient

Law of iterated logarithm and model selection consistency for generalized linear models with independent and dependent responses

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Frontiers of Mathematics in China 2021年第3期16卷 825-856页

作者： Xiaowei YANG Shuang SONG Huiming ZHANG School of Mathematics and Statistics Chaohu UniversityChaohuHefei238024China Center for Statistical Science Tsinghua UniversityBeijing100084China Department of Industrial Engineering Tsinghua UniversityBeijing100084China Department of Mathematics University of MacaoTaipa MacaoChina UMacao Zhuhai Research Institute Zhuhai519000China

We study the law of the iterated logarithm (LIL) for the maximum likelihood estimation of the parameters (as a convex optimization problem) in the generalized linear models with independent or weakly dependent (ρ-mixing) responses under mild conditions. The LIL is useful to derive the asymptotic bounds for the discrepancy between the empirical process of the log-likelihood function and the true log-likelihood. The strong consistency of some penalized likelihood-based model selection criteria can be shown as an application of the LIL. Under some regularity conditions, the model selection criterion will be helpful to select the simplest correct model almost surely when the penalty term increases with the model dimension, and the penalty term has an order higher than O(log log n) but lower than O(n). Simulation studies are implemented to verify the selection consistency of Bayesian information criterion.

关键词： Generalized linear models(GLMs) weighted scores method non-natural link function model selection consistency weakly dependent

model selection for SVM using mutative scale chaos optimization algorithm

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Journal of Shanghai University(English Edition) 2006年第6期10卷 531-534页

作者：刘清坤阙沛文费春国宋寿朋 Institute of Automatic Detection Shanghai Jiaotong University Intelligent Engineering Laboratory Department of Automation Shanghai Jiaotong University

This paper proposes a new search strategy using mutative scale chaos optimization algorithm （MSCO） for model selection of support vector machine （SVM）. It searches the parameter space of SVM with a very high efficiency and finds the optimum parameter setting for a practical classification problem with very low time cost. To demonstrate the performance of the proposed method it is applied to model selection of SVM in ultrasonic flaw classification and compared with grid search for model selection. Experimental results show that MSCO is a very powerful tool for model selection of SVM, and outperforms grid search in search speed and precision in ultrasonic flaw classification.

关键词： model selection support vector machine （SVM） mutative scale chaos optimization （MSCO） ultrasonic testing （UT） non-destructive testing （NDT）.