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Consistent Specification Testing With Nuisance Parameters Present Only Under The Alternative
, 1995
"... . The nonparametric and the nuisance parameter approaches to consistently testing statistical models are both attempts to estimate topological measures of distance between a parametric and a nonparametric fit, and neither dominates in experiments. This topological unification allows us to greatly ex ..."
Abstract

Cited by 55 (10 self)
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. The nonparametric and the nuisance parameter approaches to consistently testing statistical models are both attempts to estimate topological measures of distance between a parametric and a nonparametric fit, and neither dominates in experiments. This topological unification allows us to greatly extend the nuisance parameter approach. How and why the nuisance parameter approach works and how it can be extended bears closely on recent developments in artificial neural networks. Statistical content is provided by viewing specification tests with nuisance parameters as tests of hypotheses about Banachvalued random elements and applying the Banach Central Limit Theorem and Law of Iterated Logarithm, leading to simple procedures that can be used as a guide to when computationally more elaborate procedures may be warranted. 1. Introduction In testing whether or not a parametric statistical model is correctly specified, there are a number of apparently distinct approaches one might take. T...
Bayesian Curve Estimation By Polynomials of Random Order
, 1995
"... this paper is to provide a Bayesian (non or semiparametric) alternative to these approaches by making the order of the polynomial unknown and random (in a Bayesian sense) simultaneously with other model parameters. The resulting mixture distributions covers a rich class of models. Experience suggest ..."
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Cited by 9 (0 self)
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this paper is to provide a Bayesian (non or semiparametric) alternative to these approaches by making the order of the polynomial unknown and random (in a Bayesian sense) simultaneously with other model parameters. The resulting mixture distributions covers a rich class of models. Experience suggests that the orders of the polynomial acquiring substantial posterior probabilities are never very high and that mixing over low order polynomials can capture even quite complicated functions. Other advantages are that it is easy to differentiate, integrate and not too difficult to put constraints on the function. Of course, the global nature of polynomial fitting implies that there will be problems if the unknown curve is too wiggly and so we extend our methodology to polynomials of random order with change points and to piecewise polynomials of random order.
Estimates of Genetic Covariance Functions Assuming a Parametric Correlation Structure for Environmental Effects
, 2000
"... A random regression model for the analysis of `repeated' records in animal breeding is described which combines a random regression approach for additive genetic and other random effects with the assumption of a parametric correlation structure for within animal covariances. Both stationary and non ..."
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Cited by 4 (3 self)
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A random regression model for the analysis of `repeated' records in animal breeding is described which combines a random regression approach for additive genetic and other random effects with the assumption of a parametric correlation structure for within animal covariances. Both stationary and nonstationary correlation models involving a small number of parameters are considered. Heterogeneity in within animal variances is modelled through polynomial variance functions. Estimation of parameters describing the dispersion structure of such model by restricted maximum likelihood via an `average information' algorithm is outlined. An application to mature weight records of beef cow is given, and results are contrasted to those from analyses fitting sets of random regression coefficients for permanent environmental effects. Keywords : Repeated records, random regression model, correlation function, estimation, REML 1. Introduction Random regression (RR) models have become a preferred c...
Time to Change. Rating Changes and Policy Implications.
, 2006
"... Abstract Rating agencies are often subject to the criticism of being slow in adjusting their rating to current conditions. This paper examines the timeliness of rating changes and identifies factors which result in ’stickiness ’ of rating actions. Stickiness is characterized by not adjusting the rat ..."
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Cited by 1 (0 self)
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Abstract Rating agencies are often subject to the criticism of being slow in adjusting their rating to current conditions. This paper examines the timeliness of rating changes and identifies factors which result in ’stickiness ’ of rating actions. Stickiness is characterized by not adjusting the rating even when a marketbased estimate of default probability changes. Introducing an extended econometric model of friction the migration policy is modelled in terms of thresholds which have to be crossed by default probability estimates before an up or downgrade occurs. Default probability estimates have to change by around two notches before the rating agency reacts. The timeliness differs across the rating spectrum and over the years. During periods with high defaults and for low credit quality firms agencies tend to rate more timely.
Genet. Sel. Evol. 33 (2001) 557585 557
"... A random regression model for the analysis of "repeated " records in animal breeding is described which combines a random regression approach for additive genetic and other random effects with the assumption of a parametric correlation structure for within animal covariances. Both stationary and non ..."
Abstract
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A random regression model for the analysis of "repeated " records in animal breeding is described which combines a random regression approach for additive genetic and other random effects with the assumption of a parametric correlation structure for within animal covariances. Both stationary and nonstationary correlation models involving a small number of parameters are considered. Heterogeneity in within animal variances is modelled through polynomial variance functions. Estimation of parameters describing the dispersion structure of such model by restricted maximum likelihood via an "average information" algorithm is outlined. An application to mature weight records of beef cow is given, and results are contrasted to those from analyses fitting sets of random regression coefficients for permanent environmental effects. repeated records / random regression model / correlation function / estimation / REML 1.
and
"... In this article we extend the class of nonnegative, asymmetric kernel density estimators and propose BirnbaumSaunders (BS) and lognormal (LN) kernel density functions. The density functions have bounded support on [0,∞). Both BS and LN kernel estimators are free of boundary bias, nonnegative, wit ..."
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In this article we extend the class of nonnegative, asymmetric kernel density estimators and propose BirnbaumSaunders (BS) and lognormal (LN) kernel density functions. The density functions have bounded support on [0,∞). Both BS and LN kernel estimators are free of boundary bias, nonnegative, with natural varying shape, and achieve the optimal rate of convergence for the mean integrated squared error. We apply BS and LN kernel density estimators to high frequency intraday time duration data. The comparisons are made on several nonparametric kernel density estimators. BS and LN kernels perform better near the boundary in terms of bias reduction. c ○ 2003 Peking University Press