1,060 research outputs found
Local linear fitting under near epoch dependence: Uniform consistency with convergence rates
No full textLocal linear fitting is a popular nonparametric method in statistical and econometric modeling. Lu and Linton (2007, Econometric Theory23, 37–70) established the pointwise asymptotic distribution for the local linear estimator of a nonparametric regression function under the condition of near epoch dependence. In this paper, we further investigate the uniform consistency of this estimator. The uniform strong and weak consistencies with convergence rates for the local linear fitting are established under mild conditions. Furthermore, general results regarding uniform convergence rates for nonparametric kernel-based estimators are provided. The results of this paper will be of wide potential interest in time series semiparametric modeling.Degui Li, Zudi Lu, Oliver Linto
A. Linton Gilmore, probably between 1891 and 1905
No full textStarted the Vashon Island Press with Oliver Van Olinda.
Caption from album: A. Linton Gilmore
PH Coll 376.38
Second-order approximation for adaptive regression estimators.
No full textWe derive asymptotic expansions for semiparametric adaptive regression estimators. In particular, we derive the asymptotic distribution of the second-order effect of an adaptive estimator in a linear regression whose error density is of unknown functional form. We then show how the choice of smoothing parameters influences the estimator through higher order terms. A method of bandwidth selection is defined by minimizing the second-order mean squared error. We examine both independent and time series regressors; we also extend our results to a t-statistic. Monte Carlo simulations confirm the second order theory and the usefulness of the bandwidth selection method.
Efficient estimation of generalized additive nonparametric regression models.
No full textWe define new procedures for estimating generalized additive nonparametric regression models that are more efficient than the Linton and Härdle (1996, Biometrika 83, 529–540) integration-based method and achieve certain oracle bounds. We consider criterion functions based on the Linear exponential family, which includes many important special cases. We also consider the extension to multiple parameter models like the gamma distribution and to models for conditional heteroskedasticity.
Applied Nonparametric Methods
No full textWe review different approaches to nonparametric density and regression estimation. Kernel estimators are motivated from local averaging and solving ill-posed problems. Kernel estimators are compared to k-NN estimators, orthogonal series and splines. Pointwise and uniform confidence bands are described, and the choice of smoothing parameter is discussed. Finally, the method is applied to nonparametric prediction of time series and to semiparametric estimation.
Verses, subverses and subversions in contemporary postcolonial poetry : the arts of resistance in the works of Linton Kwesi Johnson and Lesego Rampolokeng
No full textIncludes bibliographical references (leaves 136-141).This dissertation seeks to analyse insubordination and resistance manifested in postcolonial and post-apartheid poetry as ways of subverting dominant Western discourses. More specifically, I focus my analysis on textual strategies of resistance in the poetry of Linton Kwesi Johnson and Lesego Rampolokeng. The syncretistic quality in the oeuvres of both poets is related to diaspora, hybridity and crealisation as forms of writ[h]ing against (neo)colonially-based hegemonic discourses. Postcolonial critiques at large will frame this analysis of strategies of domination and resistance, but some discussions from the domain of history, sociology and cultural studies may also enter the debate. In this regard there is a great variety of theories and arguments dealing with the contradictions and incongruities in the question of power relations interconnecting domination and resistance. This study is arranged in three pivotal debates. There is firstly an in-depth discussion of underpinning theories that deal with strategies of domination and resistance in the postcolonial domain This is a threefold task carried out by scrutinising (a) the origins of colonial discourse and its binarist tendencies, (b) the pitfalls of anticolonialist resistance based on dualistic opposites, and (c) the hybrid and insubordinate nature of resistance as an efficient alternative to transcend such binaries. Afterwards I seek to investigate how strategies of diasporic resistance and cultural hybridism employed in the poetry of Linton Kwesi Johnson can contribute to moving away from the limitations of dichotomies and also subvert hegemonic power. And finally, I look at crealisation, mockery and insubordination as strategies of resistance in the postapartheid poetry of Lesego Rampolokeng. Besides that, this project is concerned with the increasing importance of academic studies on postcolonial literatures. The present research aims therefore to analyse postcolonial and post-apartheid poems as strategic techniques to decentre dominant Western rhetoric that tries to naturalise inequalities and injustices in the relations between power holders and the powerless in both local and global contexts
On a semiparametric survival model with flexible covariate effect.
No full textA semiparametric hazard model with parametrized time but general covariate dependency is formulated and analyzed inside the framework of counting process theory. A profile likelihood principle is introduced for estimation of the parameters: the resulting estimator is n1/2-consistent, asymptotically normal and achieves the semiparametric efficiency bound. An estimation procedure for the nonparametric part is also given and its asymptotic properties are derived. We provide an application to mortality data.
A flexible semiparametric forecasting model for time series
No full textIn this paper, we propose a semiparametric procedure called the “Model Averaging MArginal Regression” (MAMAR) that is flexible for forecasting of time series. This procedure considers approximating a multivariate regression function by an affine combination of one-dimensional marginal regression functions. The weight parameters involved in the approximation are estimated by least squares on the basis of the first-stage nonparametric kernel estimates of the marginal regressions. Under some mild conditions, we have established asymptotic normality for the estimated weights and the regression function in two cases: Case I considers that the number of the covariates is fixed while Case II allows the number of the covariates depending on the sample size and diverging. As the observations are assumed to be stationary and near epoch dependent, the approach developed is applicable to both the estimation and forecasting issues in time series analysis. Furthermore, the method and result are augmented by a simulation study and illustrated by an application in forecasting the high frequency volatility of the FTSE100 index
Nonparametric inference for unbalance time series data
No full textEstimation of heteroskedasticity and autocorrelation consistent covariance matrices (HACs) is a well established problem in time series. Results have been established under a variety of weak conditions on temporal dependence and heterogeneity that allow one to conduct inference on a variety of statistics, see Newey and West (1987), Hansen (1992), de Jong and Davidson (2000), and Robinson (2004). Indeed there is an extensive literature on automating these procedures starting with Andrews (1991). Alternative methods for conducting inference include the bootstrap for which there is also now a very active research program in time series especially, see Lahiri (2003) for an overview. One convenient method for time series is the subsampling approach of Politis, Romano, andWolf (1999). This method was used by Linton, Maasoumi, andWhang (2003) (henceforth LMW) in the context of testing for stochastic dominance. This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data is unbalanced or incomplete. In this case, one can work only with the common sample, to which a standard HAC/bootstrap theory applies, but at the expense of throwing away data and perhaps losing effciency. An alternative is to use some sort of imputation method, but this requires additional modelling assumptions, which we would rather avoid.1 We show how the sampling theory changes and how to modify the resampling algorithms to accommodate the problem of missing data. We also discuss effciency and power. Unbalanced data of the type we consider are quite common in financial panel data, see for example Connor and Korajczyk (1993). These data also occur in cross-country studies.
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