Results 61 to 70 of about 1,708 (192)
Does Global Value Chain Participation Matter for Economic Upgrading? A Nonlinear Insight
The World Economy, Volume 49, Issue 5, Page 922-936, May 2026.ABSTRACT In recent decades, global value chains (GVCs) have come to dominate much of world trade. Participation in GVCs is widely regarded as a key driver of development by enabling countries to climb the value‐added ladder. However, tighter governance structures within GVCs can make these benefits uncertain.
Tao Zou
wiley +1 more source
An oracle property of the Nadaraya–Watson kernel estimator for high‐dimensional nonparametric regression [PDF]
Scandinavian Journal of Statistics, 2018 AbstractThe Nadaraya–Watson estimator is among the most studied nonparametric regression methods. A classical result is that its convergence rate depends on the number of covariates and deteriorates quickly as the dimension grows. This underscores the “curse of dimensionality” and has limited its use in high‐dimensional settings. In this paper, however,
Daniel Conn, Gang Li
openaire +3 more sources
On a Nadaraya-Watson Estimator with Two Bandwidths [PDF]
, 2020 In a regression model, we write the Nadaraya-Watson estimator of the regression function as the quotient of two kernel estimators, and propose a bandwidth selection method for both the numerator and the denominator.
Comte, Fabienne, Marie, Nicolas
core
Consistency of local linear regression estimator for mixtures with varying concentrations
Modern Stochastics: Theory and ApplicationsFinite mixtures with different regression models for different mixture components naturally arise in statistical analysis of biological and sociological data. In this paper a model of mixtures with varying concentrations is considered in which the mixing
Daniel Horbunov, Rostyslav Maiboroda
doaj +1 more source
Statistical Analysis of Stochastic Resonance in a Thresholded Detector
Austrian Journal of Statistics, 2016 A subthreshold signal may be detected if noise is added to the data. The noisy signal must be strong enough to exceed the threshold at least occasionally; but very strong noise tends to drown out the signal.
Priscilla E. Greenwood +3 more
doaj +1 more source
Model‐Free Local Partial Correlation
Australian &New Zealand Journal of Statistics, Volume 68, Issue 1, March 2026.ABSTRACT In the simple linear regression context, partial correlation measures the linear association between two variables, with the linear effects of a third control variable removed. In this paper, we investigate the local partial correlation using a kernel smoothing approach.
Li‐Shan Huang +2 more
wiley +1 more source
Cumulative Differences Between Paired Samples
Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 1, February 2026.ABSTRACT Paired samples are observations from two populations, commonly with each observed response from one population corresponding to an observed response from the other population at the same value of an ordinal covariate. Such pairs of responses at the same covariate values are called “matched pairs.” Graphing cumulative differences between the ...
Isabel Kloumann +4 more
wiley +1 more source
Bootstrap variance estimation for Nadaraya quantile estimator [PDF]
, 2009 Nadaraya quantile estimator, Order-calibration, Smoothed bootstrap, 62G05, 62G09, 62G30,
Cheung, KY +5 more
core +1 more source
Oxford Bulletin of Economics and Statistics, Volume 88, Issue 1, Page 45-56, February 2026.ABSTRACT For binary outcome models, an endogeneity correction based on nonlinear rank‐based transformations is proposed. Identification without external instruments is achieved under one of two assumptions: Either the endogenous regressor is a nonlinear function of one component of the error term, conditional on the exogenous regressors, or the ...
Alexander Mayer, Dominik Wied
wiley +1 more source
A Smoothed- Distribution Form of Nadaraya- Watson Estimation [PDF]
nonparametric regression; Nadaraya- Watson; kernel density; conditional expectation estimator; conditional variance estimator; local polynomial ...
Ralph W Bailey, John T Addison
core +1 more source