Results 41 to 50 of about 73,585 (174)

Post‐selection inference for the Cox model with interval‐censored data

Scandinavian Journal of Statistics, EarlyView.
Abstract We develop a postselection inference method for the Cox proportional hazards model with interval‐censored data, which provides asymptotically valid p‐values and confidence intervals conditional on the model selected by lasso. The method is based on a pivotal quantity that is shown to converge to a uniform distribution under local parameters ...
Jianrui Zhang, Chenxi Li, Haolei Weng
wiley   +1 more source

Sequential Quantiles via Hermite Series Density Estimation

, 2017
Sequential quantile estimation refers to incorporating observations into quantile estimates in an incremental fashion thus furnishing an online estimate of one or more quantiles at any given point in time.
Macdonald, Iain, Stephanou, Michael, Varughese, Melvin   +2 more
core   +1 more source

Nonparametric Method for a Non-inferiority Test using Confidence Interval

, 2014
Non-inferiority trials indicate whether the effect of an experimental treatment is not worse than an active control. Chen et al. (2006) and Kang (2010) proposed a test method for non-inferiority trials using confidence intervals.
Sujung Park, Dongjae Kim
semanticscholar   +1 more source

Exploration‐Based Statistical Learning for Selecting Kernel Density Estimates of Spatial Point Patterns

Transactions in GIS, Volume 29, Issue 2, April 2025.
ABSTRACT This paper addresses the use of nonparametric kernel density estimation (KDE) to estimate point‐based data density in spatial modeling using Geographic Information Systems (GIS). The paper highlights challenges in selecting the appropriate settings for generating the best fitting KDE surfaces and validating their accuracy, as many GIS packages
Michael Govorov, Giedrė Beconytė, Gennady Gienko   +2 more
wiley   +1 more source

Confidence intervals in monotone regression [PDF]

arXiv, 2023
We construct bootstrap confidence intervals for a monotone regression function. It has been shown that the ordinary nonparametric bootstrap, based on the nonparametric least squares estimator (LSE) $\hat f_n$ is inconsistent in this situation. We show, however, that a consistent bootstrap can be based on the smoothed $\hat f_n$, to be called the SLSE ...
arxiv  

Nonparametric confidence intervals for tail dependence based on copulas

, 2016
. We propose nonparametric asymptotic condence intervals for the upper and lower tail dependence coecients. These latter are obtained from condence bands established for the copula function itself and based upon three kernel-type estimators.
C. Seck, D. Ba, G. Lo
semanticscholar   +1 more source

Balanced longitudinal data clustering with a copula kernel mixture model

Canadian Journal of Statistics, Volume 53, Issue 1, March 2025.
Abstract Many common clustering methods cannot be used for clustering balanced multivariate longitudinal data in cases where the covariance of variables is a function of the time points. In this article, a copula kernel mixture model (CKMM) is proposed for clustering data of this type.
Xi Zhang, Orla A. Murphy, Paul D. McNicholas   +2 more
wiley   +1 more source

Regime‐Switching Density Forecasts Using Economists' Scenarios

Journal of Forecasting, Volume 44, Issue 2, Page 833-845, March 2025.
ABSTRACT We propose an approach for generating macroeconomic density forecasts that incorporate information on multiple scenarios defined by experts. We adopt a regime‐switching framework in which sets of scenarios (“views”) are used as Bayesian priors on economic regimes. Predictive densities coming from different views are then combined by optimizing
Graziano Moramarco
wiley   +1 more source

Hidden Truncation Models: Theory and Applications

WIREs Computational Statistics, Volume 17, Issue 1, March 2025.
ABSTRACT This review article focuses on hidden truncation models, a versatile framework for modeling a wide range of random phenomena. These models are characterized by the condition that the primary study variable(s) are observable only when a concomitant variable (or a set of concomitant variables in the multivariate case) meets specific criteria ...
Indranil Ghosh, Hon Keung Tony Ng
wiley   +1 more source