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平滑样条半参数密度模型AI 解读

Jiahui Yua, Jian Shib, Anna Liuc & Yuedong Wangd*a Department of Mathematics and Statistics, Boston University, Boston, MAb PayPal, San Jose, CAc Department of Mathematics and Statistics, University of Massachusetts, Amherst, MAd Department Statistics and Applied Probability, University of California, Santa Barbara, CAColor versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.Supplementary materials for this article are…

时间序列测量与复发事件的联合建模：空气质量与呼吸症状AI 解读

Exposure to ambient pollutants at concentrations above defined standards is a risk factor for respiratory symptoms, especially in sensitive children. Many studies have been undertaken to monitor air quality and to assess its association with respiratory symptoms. We propose a joint mixed-effects regression model of time series measures and recurrent events to analyze the air quality and respiratory symptom data from the Yale Mothers and Infants Health Study. Three mothers'…

区间删失数据的多样本检验AI 解读

The problem of testing for the equality of k distribution functions under Case 2 interval censoring is studied and a supremum-type test statistic is proposed based on the differences between the nonparametric maximum likelihood estimator and the so-called leveraged bootstrap estimator of the k underlying distributions. The proposed test is distributionfree and consistent against all alternatives. As the main results hold for a wide range of resampling sizes, a data-driven…

多元自适应样条模型中线性方向的识别AI 解读

Identifying linear directions in multivariate regression has been a statistical challenge and has attracted particular attention since projection pursuit was developed. To this end, we propose and investigate the use of linear discriminant analysis and projection Hessian directions in conjunction with multivariate adaptive regression splines. Simulation studies in a variety of settings demonstrate the usefulness of our approach in revealing both the functional forms and the…

随机筛似然与一般回归模型AI 解读

Consider a semiparametric regression model Y = f(θ, X, ϵ), where f is a known function, θ is an unknown vector, ϵ consists of a random error and possibly of some unobserved variables, and the distribution F(·) of (ϵ, X) is unspecified. This article introduces, in a general setting, new methodology for estimating θ and F(·). The proposed method constructs a profile likelihood defined on random-level sets (a random sieve). The proposed method is related to empirical likelihood…