... structure on the mean intensity functions of the event types while allowing for dependencies. A nearly rate-optimal smoothing-based estimator is proposed. An information criterion that consistently selects the number of factors is also developed. Simulation studies demonstrate the effectiveness...

... Counting process Dynamic prediction Kernel hazard estimation Nonparametric smoothing Survival analysis This paper investigates a novel approach to understanding future survival when the hazard depends on a developing marker process. Given some natural assumptions on the marker process and the marker...

... interpolating operator Gauss quadrature formulae smoothing transformation Volterra integral equation weakly singular kernel Serbian Academy of Sciences and Arts, Belgrade We are interested in the numerical computation of integrals of the form The best polynomial approximation has a weighted error...

.... Specifically, we assume that both the component and the coefficient functions in the SEDR are unknown smooth functions of location and time. This allows us to leverage spatially and temporally correlated information, transforming the curse of dimensionality into a blessing, as confirmed by our theoretical...

... differentiable Gaussian processes to model both a common viral trend and deviations at individual stations. Specifically, the common trend is modelled as an Integrated Wiener Process and station-specific signals are smoothed assuming a Matérn covariance function of order 1.5. We demonstrate the framework’s...

... of the Oxford University Press, Standard Journals Publication Model ( https://dbpia.nl.go.kr/journals/pages/open_access/funder_policies/chorus/standard_publication_model ) Summary We extend nonparametric regression smoothing splines to a context where there is endogeneity and instrumental variables...

... kernel smoothing non-parametric regression National Natural Science Foundation of China 10.13039/501100001809 12401342 72271060 Shanghai Committee of Science and Technology 24ZR1405700 National Institute of Health R01-HL138306 R01-CA236468 R01-GM140463 For risk assessment...

... almost always involves smoothing discrete observations into curves, because they are never observed in continuous time and rarely without error. Although smoothing parameters affect the subsequent inference, data-driven methods for selecting these parameters are not well-developed, frustrated...

... This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://dbpia.nl.go.kr/pages/standard-publication-reuse-rights ) Summary The smoothing parameters in a semiparametric model are estimated based on criteria such as generalized...

... estimates of a monotone curve. Laplace-approximate marginal likelihood (Wood, 2011 ) is applied for selection of the smoothing parameter (Wood et al., 2016 ). We then develop a fast and stable method for solving the non-linear equation defining the BMD estimate based on de Boor’s algorithm for B-splines...

... that follow a regression model, This paper focuses on nonparametric function estimation under smoothness constraints. Rates of convergence often limit the applications of traditional nonparametric estimation methods in high-dimensional settings, where the number of covariates is large ( Stone, 1980 , 1982...

... disaggregation of household survey data can lead to highly variable estimates of U5MR, necessitating the usage of smoothing models which borrow information across space and time. The assumptions of common smoothing models may be unrealistic when certain time periods or regions are expected to have shocks...

... is further verified by Monte Carlo simulations and analysis of real data. Distance covariance Functional data Multivariate smoothing Simultaneous pointwise independence Test of independence National Science Foundation 10.13039/100000001 DMS-2210891 Testing the dependency between two random variables...

... as a smoothing strategy to answer the threshold question. Diffenbaugh et al. also use climate models combined with machine learning to remove cyclic components from the temperature record [ 17 ]. In general, the 20-years moving-average approach will only leave linear time series unchanged, and all...

... fluctuation caused by sequencing, in order to smooth profile and capture common signature, robust smoothing and low-rank approximation are introduced. In our proposed model, robust smoothing is established with modern techniques involving total variation [ 22 ] and L-1 norm optimization [ 23 ] to reduce...

... tomography Gaussian mixture model nonparametric kernel smoothing TensorFlow National Natural Science Foundation of China 10.13039/501100001809 72171226 National Statistical Science Research Project 2023LD008 Computed tomography (CT) is an advanced 3D imaging technology capable of generating...

... presented. Sprod first projects gene expression onto a potential space, connects nearest neighbor cells to construct patterns, and then learns the denoising matrix using a shared minimization of the graph's Laplacian smoothing term and reconstruction errors [ 19 ]. For ST data without pathology images...

... of a spatial smoothing model for county-level firearm suicide mortality rates in a single year, and then extend this method to illustrate space–time smoothing of rates over several years. The following models were fitted on the full, complete mortality data for each county, without suppression. Accompanying...

... spatial HIV prevalence in women of age 15–49 in Malawi using the stratification/aggregation method we propose. classification complex survey designs HIV prevalence small area estimation spatial smoothing stratification Most low- and middle-income countries (LMICs) are not covered by Civil...

... the empirical characteristic function of the observation increments, and Liu et al. ( 2018 ) extended their method to estimating spot volatility via kernel smoothing. Based on the empirical characteristic function, Kong ( 2019 ) presented a novel test for jumps of infinite variation. All these mentioned...