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双动态最大 Copula 模型及其金融时间序列应用AI 解读

Accurately modeling time-varying dependence structures is essential for financial market analysis, particularly during periods of market stress. Recognizing that traditional copula models often fail to jointly capture dynamic dependence and tail behavior, this article proposes a double dynamic max-copula (DDMC) model that accommodates time variation while effectively characterizing tail dependence. To estimate the model, we introduce a maximum composite profile likelihood…

利用广义变系数模型检测不规则区域中的局部信号AI 解读

In spatial analysis, it is essential to understand and quantify spatial or temporal heterogeneity. This paper focuses on the generalized spatially varying coefficient model (GSVCM), a powerful framework to accommodate spatial heterogeneity by allowing regression coefficients to vary in a given spatial domain. We propose a penalized bivariate spline method for detecting local signals in GSVCM. The key idea is to use bivariate splines defined on triangulation to approximate…

结合分位数回归与形状约束的加性边界稳健估计AI 解读

We consider the estimation of the boundary of a set when it is known to be sufficiently smooth, to satisfy certain shape constraints and to have an additive structure. Our proposed method is based on spline estimation of a conditional quantile regression and is resistant to outliers and/or extreme values in the data. This work is a desirable extension of existing works in the literature and can also be viewed as an alternative to existing estimators that have been used in…

协变量维数增长时相关数据的广义可加部分线性模型估计与选择AI 解读

We propose generalized additive partial linear models for complex data which allow one to capture nonlinear patterns of some covariates, in the presence of linear components. The proposed method improves estimation efficiency and increases statistical power for correlated data through incorporating the correlation information. A unique feature of the proposed method is its capability of handling model selection in cases where it is difficult to specify the likelihood…

相关数据边际广义可加模型的一致模型选择AI 解读

We consider the generalized additive model when responses from the same cluster are correlated. Incorporating correlation in the estimation of nonparametric components for the generalized additive model is important because it improves estimation efficiency and increases statistical power for model selection. In our setting, there is no specified likelihood function for the generalized additive model, because the outcomes could be nonnormal and discrete, which makes…

用边际积分估计和检验加性模型中的变系数AI 解读

We propose marginal integration estimation and testing methods for the coefficients of varying-coefficient multivariate regression models. Asymptotic distribution theory is developed for the estimation method, which enjoys the same rate of convergence as univariate function estimation. For the test statistic, asymptotic normal theory is established. These theoretical results are derived under the fairly general conditions of absolute regularity (β-mixing). Application of the…