adapting for heteroscedasticity in linear models in a heteroscedastic linear model it is known that if the variances are a parametric function of the design then one can construct an estimate of the regression parameter which is asymptotically equivalent to the weighted least squares estimate with known variances we show that the same is true when the only thing known about the variances is that they are determined by an unknown but smooth function of the design or the mean response

local linear regression smoothers and their minimax efficiencies in this paper we introduce a smooth version of local linear regression estimators and address their advantages the mse and mise of the estimators are computed explicitly it turns out that the local linear regression smoothers have nice sampling properties and high minimax efficiency they are not only efficient in rates but also nearly efficient in constant factors in the nonparametric regression context the asymptotic minimax lower bound is developed via the heuristic of the hardest onedimensional subproblem of donoho and liu connections of the minimax risk with the modulus of continuity are made the lower bound is also applicable for estimating conditional mean regression and conditional quantiles for both fixed and random design regression problems

effect of mean on variance function estimation in nonparametric regression variance function estimation in nonparametric regression is considered and the minimax rate of convergence is derived we are particularly interested in the effect of the unknown mean on the estimation of the variance function our results indicate that contrary to the common practice it is not desirable to base the estimator of the variance function on the residuals from an optimal estimator of the mean when the mean function is not smooth instead it is more desirable to use estimators of the mean with minimal bias on the other hand when the mean function is very smooth our numerical results show that the residual based method performs better but not substantial better than the first order difference based estimator in addition our asymptotic results also correct the optimal rate claimed in hall and carroll j roy statist soc ser b51 1989 3 14

local polynomial variance function estimation the conditional variance function in a heteroscedastic nonparametric regression model is estimated by linear smoothing of squared residuals attention is focused on local polynomial smoothers both the mean and variance functions are assumed to be smooth but neither is assumed to be in a parametric family the biasing effect of preliminary estimation of the mean is studied and a degrees of freedom correction of bias is proposed the corrected method is shown to be adaptive in the sense that the variance function can be estimated with the same asymptotic mean and variance as if the mean function were known a proposal is made for using standard bandwidth selectors for estimating both the mean and variance functions the proposal is illustrated with data from the lidar method of measuring atmospheric pollutants and from turbulence model computations

on difference based variance estimation in nonparametric regression when the covariate is high dimensional summary we consider the problem of estimating the noise variance in homoscedastic nonparametric regression models for low dimensional covariates t d d 1 2 difference based estimators have been investigated in a series of papers for a given length of such an estimator difference schemes which minimize the asymptotic mean squared error can be computed for d 1 and d 2 however from numerical studies it is known that for finite sample sizes the performance of these estimators may be deficient owing to a large finite sample bias we provide theoretical support for these findings in particular we show that with increasing dimension d this becomes more drastic if d4 these estimators even fail to be consistent a different class of estimators is discussed which allow better control of the bias and remain consistent when d4 these estimators are compared numerically with kernel type estimators which are asymptotically efficient and some guidance is given about when their use becomes necessary

the estimation of residual variance in nonparametric regression summary a wide class of estimators of the residual variance in nonparametric regression is considered namely those that are quadratic in the data unbiased for linear regression and always nonnegative the minimax mean squared error estimator over a natural class of regression functions is derived this optimal estimator has an interesting structure and is closely related to a minimax estimator of the regression curve itself

adaptive variance function estimation in heteroscedastic nonparametric regression we consider a wavelet thresholding approach to adaptive variance function estimation in heteroscedastic nonparametric regression a data driven estimator is constructed by applying wavelet thresholding to the squared first order differences of the observations we show that the variance function estimator is nearly optimally adaptive to the smoothness of both the mean and variance functions the estimator is shown to achieve the optimal adaptive rate of convergence under the pointwise squared error simultaneously over a range of smoothness classes the estimator is also adaptively within a logarithmic factor of the minimax risk under the global mean integrated squared error over a collection of spatially inhomogeneous function classes numerical implementation and simulation results are also discussed

nonparametric estimation of residual variance revisited summary several difference based estimators of residual variance are compared for finite sample size since the introduction of a rather simple estimator by gasser sroka jennensteinmetz 1986 other proposals have been made here the one given by hall kay titterington 1990 is of particular interest it minimizes the asymptotic variance unfortunately it has severe problems with finite sample bias and the estimator of gasser et al 1986 proves still to be a good choice a new estimator is introduced compromising between bias and variance

on nonparametric regression for iid observations in a general setting we consider the problem of sharp optimal estimation of a response function f x in a random design nonparametric regression under a general model where a pair of observations y x has a joint density p y x p y f x x we wish to estimate the response function with optimal minimax mean integrated squared error convergence as the sample size tends to traditional regularity assumptions on the conditional density p y assumed for parameter estimation are sufficient for sharp optimal nonparametric risk convergence as well as for the existence of the best constant and rate of risk convergence this best constant is a nonparametric analog of fisher information many examples are sketched including location and scale families censored data mixture models and some well known applied examples a sequential approach and some aspects of experimental design are considered as well

central limit theorem for linear processes iences mixing sequences or associated sequences the results are important in analyzing the asymptotical properties of some estimators as well as of linear processes 4 1 introduction let be a centered sequence of random variables k 4 and let a 1 i n be a triangular array of numbers many statistical ni procedures produce estimators of the type n 1 1 s a nn i i i1 to give an example let us consider the nonlinear regression model yx gx x where gx is an unknown function and x is the noise now we fix the design points x and we get ni

estimating the variance in nonparametric regression what is a reasonable choice the exact mean squared error mse of estimators of the variance in nonparametric regression based on quadratic forms is investigated in particular two classes of estimators are compared hall kay and titterington s optimal difference based estimators and a class of ordinary difference based estimators which generalize methods proposed by rice and gasser sroka and jennen steinmetz for small sample sizes the mse of the first estimator is essentially increased by the magnitude of the integrated first two squared derivatives of the regression function it is shown that in many situations ordinary difference based estimators are more appropriate for estimating the variance because they control the bias much better and hence have a much better overall performance it is also demonstrated that rice s estimator does not always behave well data driven guidelines are given to select the estimator with the smallest mse

drift estimation in sparse sequential dynamic imaging with application to nanoscale fluorescence microscopy summary a major challenge in many modern superresolution fluorescence microscopy techniques at the nanoscale lies in the correct alignment of long sequences of sparse but spatially and temporally highly resolved images this is caused by the temporal drift of the protein structure e g due to temporal thermal inhomogeneity of the object of interest or its supporting area during the observation process we develop a simple semiparametric model for drift correction in single marker switching microscopy then we propose an m estimator for the drift and show its asymptotic normality this is used to correct the final image and it is shown that this purely statistical method is competitive with state of the art calibration techniques which require the incorporation of fiducial markers in the specimen moreover a simple bootstrap algorithm allows us to quantify the precision of the drift estimate and its effect on the final image estimation we argue that purely statistical drift correction is even more robust than fiducial tracking rendering the latter superfluous in many applications the practicability of our method is demonstrated by a simulation study and by a single marker switching application this serves as a prototype for many other typical imaging techniques where sparse observations with high temporal resolution are blurred by motion of the object to be reconstructed

efficient estimation of conditional variance functions in stochastic regression author s fan jianqing yao qiwei abstract conditional heteroscedasticity has been often used in modelling and understanding the variability of statistical data under a general setup which includes the nonlinear time series model as a special case we propose an e cient and adaptive method for estimating the conditional variance the basic idea is to apply a local linear regression to the squared residuals we demonstrate that without knowing the regression function we can estimate the conditional variance asymptotically as well as if the regression were given this asymptotic result established under the assumption that the observations are made from a strictly stationary and absolutely regular process is also veri ed via simulation further the asymptotic result paves the way for adapting an automatic bandwidth selection scheme an application with nancial data illustrates the usefulness of the proposed techniques

efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals this paper considers semiparametric efficient estimation ofconditional moment models with possibly nonsmooth residuals in unknown parametric components and unknown functions h of endogenous variables we show that 1 the penalized sieve minimum distance psmd estima tor can simultaneously achieve root n asymptotic normality ofand nonparametric optimal convergence rate of allowing for noncompact function parameter spaces 2 a simple weighted bootstrap procedure consistently estimates the limiting distribution of the psmd 3 the semi parametric efficiency bound formula of ai and chen 2003 remains valid for conditional models with nonsmooth residuals and the optimally weighted psmd estimator achieves the bound 4 the centered profiled optimally weighted psmd criterion is asymptotically chi square distributed we illustrate our theories using a partially linear quantile instrumental variables iv regression a monte carlo study and an empirical estimation of the shape invariant quantile iv engel curves

a comparison of variance estimators in nonparametric regression summary we compare two estimators of error variance both based on quadratic forms in the residuals about smoothing spline fits to data the estimators are compared over the whole range of values of the smoothing parameter as well as for data based choices of the smoothing parameter we show that the commonly used estimator of variance has the serious drawback of underestimating the error variance for small choices of the smoothing parameter this drawback is not shared by a simple but more computationally intensive alternative

residual variance and residual pattern in nonlinear regression summary a nonparametric estimator of residual variance in nonlinear regression is proposed it is based on local linear fitting asymptotically the estimator has a small bias but a larger variance compared with the parametric estimator in linear regression finite sample properties are investigated in a simulation study including a comparison with other nonparametric estimators the method is also useful for spotting heteroscedasticity and outliers in the residuals at an early stage of the data analysis a further application is checking the fit of parametric models this is illustrated for longitudinal growth data

asymptotic equivalence theory for nonparametric regression with random design this paper establishes the global asymptotic equivalence between the nonparametric regression with random design and the white noise under sharp smoothness conditions on an unknown regression or drift function the asymptotic equivalence is established by constructing explicit equivalence mappings between the nonparametric regression and the whitenoise experiments which provide synthetic observations and synthetic asymptotic solutions from any one of the two experiments with asymptotic properties identical to the true observations and given asymptotic solutions from the other the impact of such asymptotic equivalence results is that an investigation in one nonparametric problem automatically yields asymptotically analogous results in all other asymptotically equivalent nonparametric problems 1 introduction the purpose of this paper is to establish the global asymptotic equivalence between the nonparametric regression with random design and the white noise under sharp smoothness conditions on an unknown regression or drift function we establish this asymptotic equivalence by constructing explicit equivalence mappings between the nonparametric regression and the white noise problems as in brown and low 1996 for their asymptotic equivalence results the equivalence mapping from the nonparametric regression to the white noise provides synthetic observations of the white noise from the nonparametric regression such that the distributions of the synthetic observations are asymptotically equivalent to those of the true observations of the white noise for any asymptotic solution to a white noise problem the application of the solution to the synthetic observations provides an asymptotic solution to the corresponding nonparametric regression problem with identical asymptotic properties likewise the equivalence mapping from the white noise produces synthetic observations of the nonparametric regression problem and synthetic asymptotic solutions to white noise problems based on those of the corresponding nonparametric regression problems the impact of such asymptotic equivalence results is that an investigation in one nonparametric problem automatically yields asymptotically analogous results in

estimation of heteroscedasticity in regression analysis consider the regression model y i g t i varepsilon i 1 leq i leq n with nonrandom design variables t i and measurements y i for the unknown regression function g cdot we assume that the data are heteroscedastic i e e varepsilon 2 i sigma 2 i not equiv operatorname const and investigate how to estimate sigma 2 i if sigma 2 i sigma 2 t i with a smooth function sigma 2 cdot initial estimators tilde sigma 2 i can be improved by kernel smoothers and the resulting class of estimators is shown to be uniformly consistent these estimates can be used to improve the estimation of the regression function g itself in parametric and nonparametric models further applications are suggested

minimax risk over hyperrectangles and implications consider estimating the mean of a standard gaussian shift when that mean is known to lie in an orthosymmetric quadratically convex set in l 2 such sets include ellipsoids hyperrectangles and l p bodies with p 2 the minimax risk among linear estimates is within 25 of the minimax risk among all estimates the minimax risk among truncated series estimates is within a factor 4 44 of the minimax risk this implies that the difficulty of estimation a statistical quantity is measured fairly precisely by the n widths a geometric quantity if the set is not quadratically convex as in the case of l p bodies with p 2 things change appreciably minimax linear estimators may be out performed arbitrarily by nonlinear estimates the ordinary kolmogorov n widths still determine the difficulty of linear estimation but the difficulty of nonlinear estimation is tied to the inner bernstein n widths which can be far smaller essential use is made of a new heuristic that the difficulty of the hardest rectangular subproblem is equal to the difficulty of the full problem

design adaptive nonparametric regression abstract in this article we study the method of nonparametric regression based on a weighted local linear regression this method has advantages over other popular kernel methods moreover such a regression procedure has the ability of design adaptation it adapts to both random and fixed designs to both highly clustered and nearly uniform designs and even to both interior and boundary points it is shown that the local linear regression smoothers have high asymptotic efficiency i e can be 100 with a suitable choice of kernel and bandwidth among all possible linear smoothers including those produced by kernel orthogonal series and spline methods the finite sample property of the local linear regression smoother is illustrated via simulation studies nonparametric regression is frequently used to explore the association between covariates and responses there are many versions of kernel regression smoothers some estimators are not good for random designs such as in observational studies and

variance function estimation in regression the effect of estimating the mean abstract the authors consider estimation of a variance function g in regression problems such estimation requires simultaneous estimation of the mean function f we obtain sharp results on the extent to which the smoothness of f influences best rates of convergence for estimating g for example in nonparametric regression with two derivatives on g classical rates of convergence are possible if and only if the unknown f satisfies a lipschitz condition of order 1 3 or more if a parametric model is known for g then g may be estimated n 1 2 consistently if and only if f is lipschitz of order 1 2 or more optimal rates of convergence are attained by kernel estimators

variance estimation in nonparametric regression via the difference sequence method consider a gaussian nonparametric regression problem having both an unknown mean function and unknown variance function this article presents a class of difference based kernel estimators for the variance function optimal convergence rates that are uniform over broad functional classes and bandwidths are fully characterized and asymptotic normality is also established we also show that for suitable asymptotic formulations our estimators achieve the minimax rate

nonisotropic chaotic oscillations of the wave equation due to the interaction of mixing transport term and superlinear boundary condition abstract this paper studies the nonisotropic chaotic oscillations of the initial boundary value problem of one dimensional wave equation with a mixing transport term it separately considers that the boundary condition at the right end of the wave equation is a superlinear type and linear perturbation of such type each causing the total energy of the underlying system to rise and fall due to the interaction with a mixing transport term for each type of boundary condition the occurrence of nonisotropic chaotic oscillations is rigorously proved numerical examples verify the effectiveness of theoretical prediction

a simple bootstrap method for constructing nonparametric confidence bands for functions standard approaches to constructing nonparametric confidence bands for functions are frustrated by the impact of bias which generally is not estimated consistently when using the bootstrap and conventionally smoothed function estimators to overcome this problem it is common practice to either undersmooth so as to reduce the impact of bias or oversmooth and thereby introduce an explicit or implicit bias estimator however these approaches and others based on nonstandard smoothing methods complicate the process of inference for example by requiring the choice of new unconventional smoothing parameters and in the case of undersmoothing producing relatively wide bands in this paper we suggest a new approach which exploits to our advantage one of the difficulties that in the past has prevented an attractive solution to the problem the fact that the standard bootstrap bias estimator suffers from relatively high frequency stochastic error the high frequency together with a technique based on quantiles can be exploited to dampen down the stochastic error term leading to relatively narrow simple to construct confidence bands

on variance estimation in nonparametric regression a simple explicit estimator of variance in nonparametric regression is suggested based on the mean square of a sequence of residuals the estimator is valid for general error distributions has elementary bias and variance properties and is optimal to both first and second orders

algorithms for pattern matching and discovery in rna secondary structure text indexing structures provide significant advantages in the solution of many problems related to string analysis and comparison and are nowadays widely used in the analysis of biological sequences in this paper we present some applications of affix trees to problems of exact and approximate pattern matching and discovery in rna sequences by allowing bidirectional search for symmetric patterns in the sequences affix trees permit to discover and locate in the sequences patterns describing not only sequence regions but also containing information about the secondary structure that a given region could form with improvements in terms of theoretical and practical efficiency over the existing methods the search can be either exact or approximate where the approximation can be defined simultaneously both for the sequence and the structure of patterns the approach presented in this paper could provide significant help in the analysis of rna sequences where the functional motifs often involve not only sequence but also the structural constraints

non parametric estimation of residual moments and covariance the aim of non parametric regression is to model the behaviour of a response vector y in terms of an explanatory vector x based only on a finite set of empirical observations this is usually perf

semiparametric estimation of covariance matrixes for longitudinal data estimation of longitudinal data covariance structure poses significant challenges because the data usually are collected at irregular time points a viable semiparametric model for covariance matrixes has been proposed that allows one to estimate the variance function nonparametrically and to estimate the correlation function parametrically by aggregating information from irregular and sparse data points within each subject but the asymptotic properties of the quasi maximum likelihood estimator qmle of parameters in the covariance model are largely unknown we address this problem in the context of more general models for the conditional mean function including parametric nonparametric or semiparametric we also consider the possibility of rough mean regression function and introduce the difference based method to reduce biases in the context of varying coefficient partially linear mean regression models this provides a more robust estimator of the covariance function under a wider range of situati

from multiple gaussian sequences to functional data and beyond a stein estimation approach summary we expand the notion of gaussian sequence models to n experiments and propose a stein estimation strategy which relies on pooling information across experiments an oracle inequality is established to assess conditional risks given the underlying effects based on which we can quantify the size of relative error and obtain a tuning free recovery strategy that is easy to compute produces model parsimony and extends to unknown variance we show that the simultaneous recovery is adaptive to an oracle strategy which also enjoys a robustness guarantee in a minimax sense a connection to functional data is established via le cam theory for fixed and random designs under general regularity settings we further extend the model projection to general bases with mild conditions on correlation structure and conclude with potential application to other statistical problems simulated and real data examples are provided to lend empirical support to the methodology proposed and to illustrate the potential for substantial computational savings

nonparametric estimation in a regression model with additive and multiplicative noise abstract in this paper we consider an unknown functional estimation problem in a general nonparametric regression model with the feature of having both multiplicative and additive noise we propose two new wavelet estimators in this general context we prove that they achieve fast convergence rates under the mean integrated square error over besov spaces the obtained rates have the particularity of being established under weak conditions on the model a numerical study in a context comparable to stochastic frontier estimation with the difference that the boundary is not necessarily a production function supports the theory

local polynomial estimators of the volatility function in nonparametric autoregression abstract in this paper we consider a class of dynamic models in which both the conditional mean and the conditional variance volatility are unknown functions of the past we first derive probabilistic conditions under which nonparametric estimation of these functions is possible we then construct an estimator based on local polynomial fitting we examine the rates of convergence of these estimators and give a result on their asymptotic normality the local polynomial fitting of the volatility function is applied to different foreign exchange rate series we find an asymmetric u shaped smiling face form of the volatility function

nonparametric estimation of covariance structure in longitudinal data summary in longitudinal studies the effect of various treatments over time is usually of prime interest however observations on the same subject are usually correlated and any analysis should account for the underlying covariance structure a nonparametric estimate of the covariance structure is useful either as a guide to the formulation of a parametric model or as the basis for formal inference without imposing parametric assumptions the sample covariance matrix provides such an estimate when the data consist of a short sequence of measurements at a common set of time points on each of many subjects but is impractical when the data are severely unbalanced or when the sequences of measurements on individual subjects are long relative to the number of subjects the variogram of residuals from a saturated model for the mean response has previously been suggested as a nonparametric estimator for covariance structure assuming stationarity in this paper we consider kernel weighted local linear regression smoothing of sample variogram ordinates and of squared residuals to provide a nonparametric estimator for the covariance structure without assuming stationarity the value of the estimator as a diagnostic tool is demonstrated in two applications one to a set of data concerning the blood pressure of newborn babies in an intensive care unit and the other to data on the time evolution of cd4 cell numbers in hiv seroconverters

asymptotically optimal difference based estimation of variance in nonparametric regression summary we define and compute asymptotically optimal difference sequences for estimating error variance in homoscedastic nonparametric regression our optimal difference sequences do not depend on unknowns such as the mean function and provide substantial improvements over the suboptimal sequences commonly used in practice for example in the case of normal data the usual variance estimator based on symmetric second order differences is only 64 efficient relative to the estimator based on optimal second order differences the efficiency of an optimal mth order difference estimator relative to the error sample variance is 2m 2m 1 again this is for normal data and increases as the tails of the error distribution become heavier

kriging with nonparametric variance function estimation a method for fitting regression models to data that exhibit spatial correlation and heteroskedasticity is proposed it is well known that ignoring a nonconstant variance does not bias least squares estimates of regression parameters thus data analysts are easily lead to the false belief that moderate heteroskedasticity can generally be ignored unfortunately ignoring nonconstant variance when fitting variograms can seriously bias estimated correlation functions by modeling heteroskedasticity and standardizing by estimated standard deviations our approach eliminates this bias in the correlations a combination of parametric and nonparametric regression techniques is used to iteratively estimate the various components of the model the approach is demonstrated on a large data set of predicted nitrogen runoff from agricultural lands in the midwest and northern plains regions of the u s a for this data set the model comprises three main components 1 the mean function which includes farming practice variables local soil and climate characteristics and the nitrogen application treatment is assumed to be linear in the parameters and is fitted by generalized least squares 2 the variance function which contains a local and a spatial component whose shapes are left unspecified is estimated by local linear regression and 3 the spatial correlation function is estimated by fitting a parametric variogram model to the standardized residuals with the standardization adjusting the variogram for the presence of heteroskedasticity the fitting of these three components is iterated until convergence the model provides an improved fit to the data compared with a previous model that ignored the heteroskedasticity and the spatial correlation

efficient pattern matching for rna secondary structures we propose efficient methods to address key pattern matching problems in rna secondary structures using the notion of structural strings a structural string s string is composed of constant symbols and parameter symbols from the alphabets and respectively an individual symbol in the alphabet may be considered a complement of another unique symbol in the notion of matching constants parameters and complements is referred to as the structural matching s match problem which is helpful in matching rna and previously was solved by the structural suffix tree sst other approaches to rna matching that do not openly consider the s match include the use of affix data structures in this paper we provide new data structures and algorithms to address the s match problem specifically we introduce the structural suffix array and structural longest common prefix array and then identify how to s match with these data structures our new s matching solution is then used as the framework to answer various combinatorial queries encountered in matching rna secondary structures

geometrizing rates of convergence ii consider estimating a functional t f of an unknown distribution f in mathbf f from data x 1 cdots x n i i d f let omega varepsilon denote the modulus of continuity of the functional t over mathbf f computed with respect to hellinger distance for well behaved loss functions l t we show that inf t n sup mathbf f e fl t n t f is equivalent to l omega n 1 2 to within constants whenever t is linear and mathbf f is convex the same conclusion holds in three nonlinear cases estimating the rate of decay of a density estimating the mode and robust nonparametric regression we study the difficulty of testing between the composite infinite dimensional hypotheses h 0 t f leq t and h 1 t f geq t delta our results hold in the cases studied because the difficulty of the full infinite dimensional composite testing problem is comparable to the difficulty of the hardest simple two point testing subproblem

analysis of longitudinal data with semiparametric varying coefficient mean covariance models abstract the efficient estimation of regression coefficients in the longitudinal data analysis requires a correct specification of the covariance structure existing approaches usually focus on modeling the mean with specification of certain covariance structures which may lead to inefficient or biased estimators of parameters in the mean if misspecification occurs in this article we propose a novel data driven approach based on semiparametric varying coefficient models to model the mean and the covariance simultaneously motivated by the modified cholesky decomposition an iterative estimation method is proposed consisting of an orthogonality based technique for parameters an adaptive jump preserving estimation method for varying coefficients a modification of local linear smoothing technique for the autoregressive coefficient function and a kernel smoothing technique for the variance function theoretical properties of the resulting estimators including uniform consistency and asymptotic normality are explicitly studied under certain mild conditions simulation studies are carried out to evaluate the efficacy of the proposed methods and an analysis of a real data example is provided for illustration

a graphical method for estimating the residual variance in nonparametric regression summary if one wishes to make inferences about a regression then an accurate estimator of the residual variance is required however except in the simplest cases all estimators of variance are biased by an amount which depends on the unknown regression here a graphical procedure is proposed which indicates whether the bias in a particular estimate is small this procedure is studied in some detail for estimators which are weighted sums of squares of divided differences of the data and leads to a practical method of variance estimation