• Title, Summary, Keyword: smoothing splines

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On the regularization with nonlinear splines

  • Chung, S.K.;Joe, S.M.
    • Communications of the Korean Mathematical Society
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    • v.12 no.1
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    • pp.165-176
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    • 1997
  • In order to overcome computational ill-posedness which arises when we solve the least square problems, nonlinear smoothing splines are used. The existence and the convergence on nonlinear smoothing spline are shown with numerical experiments.

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Nonparametric Regression with Genetic Algorithm (유전자 알고리즘을 이용한 비모수 회귀분석)

  • Kim, Byung-Do;Rho, Sang-Kyu
    • Asia pacific journal of information systems
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    • v.11 no.1
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    • pp.61-73
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    • 2001
  • Predicting a variable using other variables in a large data set is a very difficult task. It involves selecting variables to include in a model and determining the shape of the relationship between variables. Nonparametric regression such as smoothing splines and neural networks are widely-used methods for such a task. We propose an alternative method based on a genetic algorithm(GA) to solve this problem. We applied GA to regression splines, a nonparametric regression method, to estimate functional forms between variables. Using several simulated and real data, our technique is shown to outperform traditional nonparametric methods such as smoothing splines and neural networks.

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The Use of The Spectral Properties of Basis Splines in Problems of Signal Processing

  • Nasiritdinovich, Zaynidinov Hakim;Egamberdievich, MirzayevAvaz;Panjievich, Khalilov Sirojiddin
    • Journal of Multimedia Information System
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    • v.5 no.1
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    • pp.63-66
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    • 2018
  • In this work, the smoothing and the interpolation basis splines are analyzed. As well as the possibility of using the spectral properties of the basis splines for digital signal processing are shown. This takes into account the fact that basic splines represent finite, piecewise polynomial functions defined on compact media.

A FRAMEWORK TO UNDERSTAND THE ASYMPTOTIC PROPERTIES OF KRIGING AND SPLINES

  • Furrer Eva M.;Nychka Douglas W.
    • Journal of the Korean Statistical Society
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    • v.36 no.1
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    • pp.57-76
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    • 2007
  • Kriging is a nonparametric regression method used in geostatistics for estimating curves and surfaces for spatial data. It may come as a surprise that the Kriging estimator, normally derived as the best linear unbiased estimator, is also the solution of a particular variational problem. Thus, Kriging estimators can also be interpreted as generalized smoothing splines where the roughness penalty is determined by the covariance function of a spatial process. We build off the early work by Silverman (1982, 1984) and the analysis by Cox (1983, 1984), Messer (1991), Messer and Goldstein (1993) and others and develop an equivalent kernel interpretation of geostatistical estimators. Given this connection we show how a given covariance function influences the bias and variance of the Kriging estimate as well as the mean squared prediction error. Some specific asymptotic results are given in one dimension for Matern covariances that have as their limit cubic smoothing splines.

Boundary Corrected Smoothing Splines

  • Kim, Jong-Tae
    • Journal of the Korean Data and Information Science Society
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    • v.9 no.1
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    • pp.77-88
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    • 1998
  • Smoothing spline estimators are modified to remove boundary bias effects using the technique proposed in Eubank and Speckman (1991). An O(n) algorithm is developed for the computation of the resulting estimator as well as associated generalized cross-validation criteria, etc. The asymptotic properties of the estimator are studied for the case of a linear smoothing spline and the upper bound for the average mean squared error of the estimator given in Eubank and Speckman (1991) is shown to be asymptotically sharp in this case.

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Negative Binomial Varying Coefficient Partially Linear Models

  • Kim, Young-Ju
    • Communications for Statistical Applications and Methods
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    • v.19 no.6
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    • pp.809-817
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    • 2012
  • We propose a semiparametric inference for a generalized varying coefficient partially linear model(VCPLM) for negative binomial data. The VCPLM is useful to model real data in that varying coefficients are a special type of interaction between explanatory variables and partially linear models fit both parametric and nonparametric terms. The negative binomial distribution often arise in modelling count data which usually are overdispersed. The varying coefficient function estimators and regression parameters in generalized VCPLM are obtained by formulating a penalized likelihood through smoothing splines for negative binomial data when the shape parameter is known. The performance of the proposed method is then evaluated by simulations.

Semiparametric Regression Splines in Matched Case-Control Studies

  • Kim, In-Young;Carroll, Raymond J.;Cohen, Noah
    • Proceedings of the Korean Statistical Society Conference
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    • pp.167-170
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    • 2003
  • We develop semiparametric methods for matched case-control studies using regression splines. Three methods are developed: an approximate crossvalidation scheme to estimate the smoothing parameter inherent in regression splines, as well as Monte Carlo Expectation Maximization (MCEM) and Bayesian methods to fit the regression spline model. We compare the approximate cross-validation approach, MCEM and Bayesian approaches using simulation, showing that they appear approximately equally efficient, with the approximate cross-validation method being computationally the most convenient. An example from equine epidemiology that motivated the work is used to demonstrate our approaches.

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A Second Order Smoother (이차 평활스플라인)

  • 김종태
    • The Korean Journal of Applied Statistics
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    • v.11 no.2
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    • pp.363-376
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    • 1998
  • The linear smoothing spline estimator is modified to remove boundary bias effects. The resulting estimator can be calculated efficiently using an O(n) algorithm that is developed for the computation of fitted values and associated smoothing parameter selection criteria. The asymptotic properties of the estimator are studied for the case of a uniform design. In this case the mean squared error properties of boundary corrected linear smoothing splines are seen to be asymptotically competitive with those for standard second order kernel smoothers.

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Efficient estimation and variable selection for partially linear single-index-coefficient regression models

  • Kim, Young-Ju
    • Communications for Statistical Applications and Methods
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    • v.26 no.1
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    • pp.69-78
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    • 2019
  • A structured model with both single-index and varying coefficients is a powerful tool in modeling high dimensional data. It has been widely used because the single-index can overcome the curse of dimensionality and varying coefficients can allow nonlinear interaction effects in the model. For high dimensional index vectors, variable selection becomes an important question in the model building process. In this paper, we propose an efficient estimation and a variable selection method based on a smoothing spline approach in a partially linear single-index-coefficient regression model. We also propose an efficient algorithm for simultaneously estimating the coefficient functions in a data-adaptive lower-dimensional approximation space and selecting significant variables in the index with the adaptive LASSO penalty. The empirical performance of the proposed method is illustrated with simulated and real data examples.