Ja-Yong Koo

Spline Estimation of Discontinuous Regression Functions

Abstract This article deals with regression function estimation when the regression function is smooth at all but a finite number of points. An important question is: How can one produce discontinuous output without knowledge of the location of discontinuity points? Unlike most commonly used smoothers that tend to blur discontinuity in the data, we need to find a smoother that can detect such discontinuity. In this article, linear splines are used to estimate discontinuous regression functions. A procedure of knot-merging is introduced for the estimation of regression functions near discontinuous points. The basic idea is to use multiple knots for spline estimates. We use an automatic procedure involving the least squares method, stepwise knot addition, stepwise basis deletion, knot-merging, and the Bayes information criterion to select the final model. The proposed method can produce discontinuous outputs. Numerical examples using both simulated and real data are given to illustrate the performance of th...

Poisson intensity estimation for tomographic data using a wavelet shrinkage approach

We consider a two-dimensional (2-D) problem of positron-emission tomography (PET) where the random mechanism of the generation of the tomographic data is modeled by Poisson processes. The goal is to estimate the intensity function which corresponds to emission density. Using the wavelet-vaguelette decomposition (WVD), we propose an estimator based on thresholding of empirical vaguelette coefficients which attains the minimax rates of convergence on Besov function classes. Furthermore, we construct an adaptive estimator which attains the optimal rate of convergence up to a logarithmic term.

On spline estimators and prediction intervals in nonparametric regression

The quantile regression function gives the quantile in the conditional distribution of a response variable given the value of a covariate. It can be used to measure the effect of covariates not only in the center of a population, but also in the upper and lower tails. Moreover, it provides prediction intervals that do not rely on normality or other distributional assumptions. In a nonparametric setting, we explore a class of quantile regression spline estimators of the quantile regression function. We consider an automatic knot selection procedure involving a linear programming method, stepwise knot addition using a modified AIC, and stepwise knot deletion using a modified BIC. Because the methods estimate quantile regression functions, they possess an inherent robustness to extreme observations in the response values. We investigate the performance of prediction intervals based on automatic quantile regression splines and find that the loss of efficiency of this procedure is minimal in the normal linear homoscedastic model. In heteroscedastic linear models, it outperforms the classical normal theory prediction interval. A data example is provided to illustrate the use of the proposed methods.

Image reconstruction using the wavelet transform for positron emission tomography

We conducted positron emission tomography (PET) image reconstruction experiments using the wavelet transform. The Wavelet-Vaguelette decomposition was used as a framework from which expressions for the necessary wavelet coefficients might be derived, and then the wavelet shrinkage was applied to the wavelet coefficients for the reconstruction (WVS). The performances of WVS were evaluated and compared with those of the filtered back-projection (FBP) using software phantoms, physical phantoms, and human PET studies. The results demonstrated that WVS gave stable reconstruction over the range of shrinkage parameters and provided better noise and spatial resolution characteristics than FBP.

Classification of gene functions using support vector machine for time-course gene expression data

Since most biological systems are developmental and dynamic, time-course gene expression profiles provide an important characterization of gene functions. Assigning functions for genes with unknown functions based on time-course gene expressions is an important task in functional genomics. Recently, various methods have been proposed for the classification of gene functions based on time-course gene expression data. In this paper, we consider the classification of gene functions from functional data analysis viewpoint, where a functional support vector machine is adopted. The functional support vector machine can model temporal effects of time-course gene expression data by incorporating the coefficients as well as the basis matrix obtained from a finite expansion of gene expressions on a set of basis functions. We apply the functional support vector machine to both real microarray and simulated data. Our results indicate that the functional support vector machine is effective in discriminating gene functions of time-course gene expressions with predefined functions. The method also provides valuable functional information about interactions between genes and allows the assignment of new functions to genes with unknown functions.

Structured polychotomous machine diagnosis of multiple cancer types using gene expression

MOTIVATION The problem of class prediction has received a tremendous amount of attention in the literature recently. In the context of DNA microarrays, where the task is to classify and predict the diagnostic category of a sample on the basis of its gene expression profile, a problem of particular importance is the diagnosis of cancer type based on microarray data. One method of classification which has been very successful in cancer diagnosis is the support vector machine (SVM). The latter has been shown (through simulations) to be superior in comparison with other methods, such as classical discriminant analysis, however, SVM suffers from the drawback that the solution is implicit and therefore is difficult to interpret. In order to remedy this difficulty, an analysis of variance decomposition using structured kernels is proposed and is referred to as the structured polychotomous machine. This technique utilizes Newton-Raphson to find estimates of coefficients followed by the Rao and Wald tests, respectively, for addition and deletion of import vectors. RESULTS The proposed method is applied to microarray data and simulation data. The major breakthrough of our method is efficiency in that only a minimal number of genes that accurately predict the classes are selected. It has been verified that the selected genes serve as legitimate markers for cancer classification from a biological point of view. AVAILABILITY All source codes used are available on request from the authors.

Logspline Deconvolution in Besov Space

ABSTRACT. In this paper we consider logspline density estimation for random variables which are contaminated with random noise. In the logspline density estimation for data without noise, the logarithm of an unknown density function is estimated by a polynomial spline, the unknown parameters of which are given by maximum likelihood. When noise is present, B‐splines and the Fourier inversion formula are used to construct the logspline density estimator of the unknown density function. Rates of convergence are established when the log‐density function is assumed to be in a Besov space. It is shown that convergence rates depend on the smoothness of the density function and the decay rate of the characteristic function of the noise. Simulated data are used to show the finite‐sample performance of inference based on the logspline density estimation.

Asymptotic Minimax Bounds for Stochastic Deconvolution Over Groups

This paper examines stochastic deconvolution over noncommutative compact Lie groups. This involves Fourier analysis on compact Lie groups as well as convolution products over such groups. An observation process consisting of a known impulse response function convolved with an unknown signal with additive white noise is assumed. Data collected through the observation process then allow us to construct an estimator of the signal. Signal recovery is then assessed through integrated mean squared error for which the main results show that asymptotic minimaxity depends on smoothness properties of the impulse response function. Thus, if the Fourier transform of the impulse response function is bounded polynomially, then the asymptotic minimax signal recovery is polynomial, while if the Fourier transform of the impulse response function is exponentially bounded, then the asymptotic minimax signal recovery is logarithmic. Such investigations have been previously considered in both the engineering and statistics literature with applications in among others, medical imaging, robotics, and polymer science.

B-Spline deconvolution based on the Em algorithm

B-splines are considered for the decon volution problem of estimating a probability density function when the sample observations are contaminated with random noise. In the logspline method of density estimation, the logarithm of the unknown density function is approximated by a polynomial spline, the unknown parameters of which are estimated by maximum likelihood. Based on the logspline method, a fully automated procedure involving the EM algorithm, stepwise knot deletion and BIC has been developed for decon volution. Numerical examples using simulated data are given to show the performance of the B-spline deconvolution.

Wavelet density estimation by approximation of log-densities

Probability density estimation is considered when log-density function belongs to the Besov function class Bspq. It is shown that n-2s/(2s+1) is a lower rate of convergence in Kullback-Leibler distance. Density functions are estimated by the maximum likelihood method in sequences of regular exponential families based on wavelet basis functions.