Results 251 to 260 of about 442,883 (292)

Model checking with nonparametric curves

Biometrika, 1991
SUMMARY The use of local likelihood curve estimates in model checking is described. Test statistics are based on deviance differences, and are interpreted using nonparametric bootstrap Monte Carlo. Some theoretical aspects are discussed. Methods are illustrated for regression with discrete data.
D. Firth, J. Glosup, D. V. Hinkley
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Nonparametric modeling of single neuron

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008
Nonlinear dynamic models were built with Volterra Lagurre kernel method to characterize the input-output properties of single hippocampal CA1 pyramidal neurons. Broadband Poisson random impulse trains with a 2 Hz mean frequency, which include the majorities of the spike patterns in behaving rats, were used to stimulate the Schaffer collaterals ...
Ude, Lu, Dong, Song, Theodore W, Berger
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Nonparametric Statistical Models

2015
In this chapter we introduce and motivate the statistical models that will be considered in this book. Some of the materials depend on basic facts developed in subsequent chapters – mostly the basic Gaussian process and Hilbert space theory. This will be hinted at when necessary. Very generally speaking, a statistical model for a random observation
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Bayesian Semi-Nonparametric Arch Models

The Review of Economics and Statistics, 1994
A Bayesian seminonparametric approach to ARCH models is developed with the advantage that small sample results are obtained even when the likelihood function is subject to nonlinear inequality constraints (as in the ARCH models used in this paper). The seminonparametric nature of the approach allows for the relaxation of the assumption of normal errors.
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Nonparametric models and their estimation

Allgemeines Statistisches Archiv, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A nonparametric general linear model

Computers and Biomedical Research, 1972
Abstract A matrix formulation of the Kruskal-Wallis analysis of variance is presented. This formulation illustrates the parallel nature of the parametric general linear model and the Kruskal-Wallis model. Using the matrix formulation, it is shown that the Kruskal-Wallis method can be implemented on a digital computer as a special case of a general ...
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Nonparametric Models for Dichotomous Responses

1997
The development of nonparametric approaches to psychometric and sociometric measurement dates back to the days before the establishment of regular item response theory (IRT). It has its roots in the early manifestations of scalogram analysis (Guttman, 1950), latent structure analysis (Lazarsfeld, 1950), and latent trait theory (Lord, 1953).
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NONPARAMETRIC COVARIANCE MODEL.

Statistica Sinica
There has been considerable attention on estimation of conditional variance function in the literature. We propose here a nonparametric model for conditional covariance matrix. A kernel estimator is developed accordingly, its asymptotic bias and variance are derived, and its asymptotic normality is established. A real data example is used to illustrate
Jianxin, Yin, Zhi, Geng, Runze, Li, Hansheng, Wang   +3 more
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Nonparametric Spline Models

2003
Smoothing by spline functions is a tried and tested method that is used in many different fields. As mentioned in the introductory chapter, the variational approach of one-dimensional splines provides an immediate introduction to the major issues inherent in modeling (regularization, Bayesian estimation, robustness, estimation of the regularization ...
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies

Ca-A Cancer Journal for Clinicians, 2022
Paolo Tarantino, Roberto Carmagnani Pestana, Shanu Modi   +2 more
exaly