... of these inference tools. The proposed method is applied to grocery shopping data, for which an interpretable factor structure is obtained. Counting process Factor analysis Information criterion Kernel smoothing Marginal modelling Page 1 of 14 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26...

... 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...

...-validation after pooling all the curves together quickly become computationally unfeasible due to the large number of data points. In this paper, we propose a new data-driven, adaptive kernel smoothing, specifically tailored for functional principal components analysis through the derivation of sharp...

... 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...

... 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...

... are only short-run in many cases. higher order dynamics kernel smoothing missing observations portmanteau test zeros C01 C32 Romanian Ministry of Research, Innovation and Digitization PN-III-P4-ID-PCE-2020-1112 Fondecyt 10.13039/501100002850 1201898 When P ( a t = 1 ) = 1...

... that implements the kernel smoothing method in Hoover et al. (1998) and its built-in cross-validation procedure to choose the bandwidth. As a comparison, we consider the following estimators, that is, the Oracle estimator with the known synchronized true values of X, the KW estimator ( Cao et al., 2015...

... to provide an illustration. change point convergence rate functional data kernel smoothing phase transition structural break Academic Research Funds R-155-000-205-114 R-155-000-195-114 Tier 2 MOE funds in Singapore MOE2017-T2-2-082 R-155-000-197-112 R-155-000-197-113 National Science...

... coupled with the kernel smoothing and EM algorithm are developed in Section 2.2 . Computational issues about estimation at the boundaries and the weight selection are discussed in Section 2.3 . In Section 3 , we establish asymptotic properties of the nonparametric and parametric estimators...

...{\scriptscriptstyle T} }} X_j) \int_{0}^{\tau} \int_{0}^{\tau} I( uKernel smoothing Rate function Recurrent event Recurrent event data have a wide application in various fields, including medicine...

...-validation kernel smoothing local pseudo-partial log-likelihood nonparametric analysis Shanghai Jiao Tong University 10.13039/501100004921 STAR Grant 20190102 Yu National Natural Science Foundation of China 10.13039/501100001809 11671256 Yu In numerous clinical studies, subjects often...

... in population mean estimation via simulations and a real data example. Our results indicate that particularly boosting methods represent promising alternatives to logistic regression and result in KW estimates with lower bias in a variety of settings, without increasing variance. Kernel smoothing Machine...

...) and the noninformative sampling mechanism assumption for sample B. However, their proposed method relies on a correctly specified model for m(x). In Section 3, we relax this strong assumption and propose nonparametric and semiparametric mass imputation using kernel smoothing and generalized...

... via a simulation study. We apply our proposed procedures to an AIDS clinical trial dataset to examine a trajectory of CD4 counts as a potential surrogate. functional data kernel smoothing longitudinal data nonparametric analysis surrogate markers National Institute of Diabetes and Digestive...

...Lingxiao Wang; Barry I. Graubard; Hormuzd A. Katki; and Yan Li Cohort studies Complex survey sample Jackknife variance estimation Kernel smoothing Propensity score weighting Taylor series linearization variance In this section, we propose a KW approach to create pseudoweights for the cohort...

... cancer risk in an electronic medical record study and the effect of smoking on C-reactive protein in the Framingham Offspring Study. causal inference double-robustness electronic medical records kernel smoothing regularization semiparametric efficiency National Institutes of Health 10.13039...

.../standard_publication_model ) Summary This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. We first estimate the sample mean, autocovariances, and autocorrelations for each unit and then apply kernel smoothing to compute...

...Layla Parast; Tianxi Cai; Lu Tian kernel smoothing nonparametric method resampling surrogate survival analysis testing National Institute of Diabetes and Digestive and Kidney Diseases 10.13039/100000062 R01DK11835 R21DK1031184 Correspondence Layla Parast, Statistics Group, RAND...

... the accuracy of this bootstrap procedure for various models, testing procedures and sample sizes. Empirical distribution function Kernel smoothing Linear regression Location model Nonparametric regression Consider the model (1) \begin{eqnarray} Y = m(X) + \varepsilon, \end{eqnarray} where...

... Bootstrap Derivatives Factor-by-curve interaction Kernel smoothing Non-parametric regression Overexploitation or overfishing is the removal of marine living resources to levels that cannot sustain viable populations. A barnacle fishery, which commands high prices in the market, provides a perfect...