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Arnes JI, Hapfelmeier A, Horsch A, Braaten T. Greedy knot selection algorithm for restricted cubic spline regression. FRONTIERS IN EPIDEMIOLOGY 2023; 3:1283705. [PMID: 38455941 PMCID: PMC10910934 DOI: 10.3389/fepid.2023.1283705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 11/17/2023] [Indexed: 03/09/2024]
Abstract
Non-linear regression modeling is common in epidemiology for prediction purposes or estimating relationships between predictor and response variables. Restricted cubic spline (RCS) regression is one such method, for example, highly relevant to Cox proportional hazard regression model analysis. RCS regression uses third-order polynomials joined at knot points to model non-linear relationships. The standard approach is to place knots by a regular sequence of quantiles between the outer boundaries. A regression curve can easily be fitted to the sample using a relatively high number of knots. The problem is then overfitting, where a regression model has a good fit to the given sample but does not generalize well to other samples. A low knot count is thus preferred. However, the standard knot selection process can lead to underperformance in the sparser regions of the predictor variable, especially when using a low number of knots. It can also lead to overfitting in the denser regions. We present a simple greedy search algorithm using a backward method for knot selection that shows reduced prediction error and Bayesian information criterion scores compared to the standard knot selection process in simulation experiments. We have implemented the algorithm as part of an open-source R-package, knutar.
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Affiliation(s)
- Jo Inge Arnes
- Department of Computer Science, Faculty of Science and Technology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Alexander Hapfelmeier
- Institute of AI and Informatics in Medicine, TUM School of Medicine, Technical University of Munich, Munich, Germany
| | - Alexander Horsch
- Department of Computer Science, Faculty of Science and Technology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Tonje Braaten
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
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Franco G, de Souza CPE, Garcia NL. Aggregated functional data model applied on clustering and disaggregation of UK electrical load profiles. J R Stat Soc Ser C Appl Stat 2023. [DOI: 10.1093/jrsssc/qlac006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Abstract
Understanding electrical energy demand at the consumer level plays an important role in planning the distribution of electrical networks and offering of off-peak tariffs, but observing individual consumption patterns is still expensive. On the other hand, aggregated load curves are normally available at the substation level. The proposed methodology separates substation aggregated loads into estimated mean consumption curves, called typical curves, including information given by explanatory variables. In addition, a model-based clustering approach for substations is proposed based on the similarity of their consumers’ typical curves and covariance structures. The methodology is applied to a real substation load monitoring dataset from the UK and tested in eight simulated scenarios.
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Affiliation(s)
- Gabriel Franco
- Department of Statistics, Universidade Estadual de Campinas (UNICAMP) , São Paulo , Brazil
| | - Camila P E de Souza
- Department of Statistical and Actuarial Sciences, The University of Western Ontario , London , Canada
| | - Nancy L Garcia
- Department of Statistics, Universidade Estadual de Campinas (UNICAMP) , São Paulo , Brazil
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Dai W, Song Y, Wang D. A subsampling method for regression problems based on minimum energy criterion. Technometrics 2022. [DOI: 10.1080/00401706.2022.2127915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Wenlin Dai
- Center for Applied Statistics, Institute of Statistics and Big Data, Renmin University of China, Beijing, China
| | - Yan Song
- Center for Applied Statistics, Institute of Statistics and Big Data, Renmin University of China, Beijing, China
| | - Dianpeng Wang
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China
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Meng C, Yu J, Chen Y, Zhong W, Ma P. Smoothing splines approximation using Hilbert curve basis selection. J Comput Graph Stat 2022; 31:802-812. [PMID: 36407675 PMCID: PMC9674117 DOI: 10.1080/10618600.2021.2002161] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 05/21/2021] [Accepted: 09/23/2021] [Indexed: 01/14/2023]
Abstract
Smoothing splines have been used pervasively in nonparametric regressions. However, the computational burden of smoothing splines is significant when the sample size n is large. When the number of predictors d ≥ 2 , the computational cost for smoothing splines is at the order of O(n 3) using the standard approach. Many methods have been developed to approximate smoothing spline estimators by using q basis functions instead of n ones, resulting in a computational cost of the order O(nq 2). These methods are called the basis selection methods. Despite algorithmic benefits, most of the basis selection methods require the assumption that the sample is uniformly-distributed on a hyper-cube. These methods may have deteriorating performance when such an assumption is not met. To overcome the obstacle, we develop an efficient algorithm that is adaptive to the unknown probability density function of the predictors. Theoretically, we show the proposed estimator has the same convergence rate as the full-basis estimator when q is roughly at the order of O[n 2d/{(pr+1)(d +2)}] , where p ∈[1, 2] and r ≈ 4 are some constants depend on the type of the spline. Numerical studies on various synthetic datasets demonstrate the superior performance of the proposed estimator in comparison with mainstream competitors.
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Affiliation(s)
- Cheng Meng
- Institute of Statistics and Big Data, Renmin University of China
| | - Jun Yu
- School of Mathematics and Statistics, Beijing Institute of Technology
| | | | | | - Ping Ma
- Department of Statistics, University of Georgia
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Zhang X, Datta GS, Ma P, Zhong W. Bayesian spline smoothing with ambiguous penalties. CAN J STAT 2021. [DOI: 10.1002/cjs.11655] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Xinlian Zhang
- Division of Biostatistics and Bioinformatics University of California San Diego La Jolla California USA
| | - Gauri S. Datta
- Department of Statistics University of Georgia Athens Georgia USA
| | - Ping Ma
- Department of Statistics University of Georgia Athens Georgia USA
| | - Wenxuan Zhong
- Department of Statistics University of Georgia Athens Georgia USA
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Stable activation-based regression with localizing property. COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS 2021. [DOI: 10.29220/csam.2021.28.3.281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Kobayashi M, Hoshina K, Nemoto Y, Takagi S, Shojima M, Hayakawa M, Yamada S, Oshima M. A penalized spline fitting method to optimize geometric parameters of arterial centerlines extracted from medical images. Comput Med Imaging Graph 2020; 84:101746. [PMID: 32745635 DOI: 10.1016/j.compmedimag.2020.101746] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 04/21/2020] [Accepted: 06/04/2020] [Indexed: 10/23/2022]
Abstract
In order to grasp the spatial and temporal evolution of vascular geometry, three-dimensional (3D) arterial bending structure and geometrical changes of arteries and stent grafts (SG) must be quantified using geometrical parameters such as curvature and torsion along the vasculature centerlines extracted from medical images. Here, we develop a robust method for constructing smooth centerlines based on a spline fitting method (SFM) such that the optimized geometric parameters of curvature and torsion can be obtained independently of digitization noise in the images. Conventional SFM consists of the 3rd degree spline basis function and 2nd derivative penalty term. In contrast, the present SFM uses the 5th degree spline basis function and 3rd and 4th derivative penalty terms, the coefficients of which are derived by the Akaike information criterion. The results show that the developed SFM can reduce the errors of curvature and torsion compared to conventional SFM. We then apply the present SFM to the centerline of the SG in an abdominal aortic aneurysm (AAA), and those of bilateral internal carotid arteries (ICA) in 6 cases: 3 cases with aneurysms and 3 cases without any aneurysm. The SG centerlines were obtained from temporal medical images at three scan times. The strong peak of the curvature could be clearly observed in the distal area of the SG, the inversion of the torsion at 0 months in the middle area of SG disappeared over time, and the torsions around the SG bifurcation at the three time periods were inverted. The curvature-torsion graphs along the ICA centerlines superimposing five aneurysmal positions were useful for investigating the relationship between arterial bending structure and aneurysmal positions. Both ICAs had curvature peak values higher than 0.4 within the ICA syphons. The ICA torsion graphs indicated that left and right ICA tended to be a right- and left-handed helix, respectively. In the left ICA syphon, the biggest aneurysm could be observed downstream of the salient torsion inversion. All aneurysms for 3 cases were positioned at the downstream of the inverted torsion.
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Affiliation(s)
- Masaharu Kobayashi
- Graduate School of Interdisciplinary Information Studies, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
| | - Katsuyuki Hoshina
- Division of Vascular Surgery, Department of Surgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Youkou Nemoto
- Division of Vascular Surgery, Department of Surgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Shu Takagi
- Department of Mechanical Engineering, Graduate School of Engineering, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan.
| | - Masaaki Shojima
- Department of Neurosurgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Motoharu Hayakawa
- Department of Neurosurgery, Fujita Health University, 1-98 Kengakugakubo, Kutsukake-cho, Toyoake, Aichi 470-1192, Japan.
| | - Shigeki Yamada
- Department of Neurosurgery, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu, Shiga 520-2192, Japan.
| | - Marie Oshima
- Interfaculty in Information Studies/Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
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Meng C, Zhang X, Zhang J, Zhong W, Ma P. More efficient approximation of smoothing splines via space-filling basis selection. Biometrika 2020; 107:723-735. [PMID: 32831354 DOI: 10.1093/biomet/asaa019] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Indexed: 11/15/2022] Open
Abstract
We consider the problem of approximating smoothing spline estimators in a nonparametric regression model. When applied to a sample of size [Formula: see text], the smoothing spline estimator can be expressed as a linear combination of [Formula: see text] basis functions, requiring [Formula: see text] computational time when the number [Formula: see text] of predictors is two or more. Such a sizeable computational cost hinders the broad applicability of smoothing splines. In practice, the full-sample smoothing spline estimator can be approximated by an estimator based on [Formula: see text] randomly selected basis functions, resulting in a computational cost of [Formula: see text]. It is known that these two estimators converge at the same rate when [Formula: see text] is of order [Formula: see text], where [Formula: see text] depends on the true function and [Formula: see text] depends on the type of spline. Such a [Formula: see text] is called the essential number of basis functions. In this article, we develop a more efficient basis selection method. By selecting basis functions corresponding to approximately equally spaced observations, the proposed method chooses a set of basis functions with great diversity. The asymptotic analysis shows that the proposed smoothing spline estimator can decrease [Formula: see text] to around [Formula: see text] when [Formula: see text]. Applications to synthetic and real-world datasets show that the proposed method leads to a smaller prediction error than other basis selection methods.
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Affiliation(s)
- Cheng Meng
- Department of Statistics, University of Georgia, 310 Herty Dr., Athens, Georgia 30602, U.S.A
| | - Xinlian Zhang
- Department of Statistics, University of Georgia, 310 Herty Dr., Athens, Georgia 30602, U.S.A
| | - Jingyi Zhang
- Department of Statistics, University of Georgia, 310 Herty Dr., Athens, Georgia 30602, U.S.A
| | - Wenxuan Zhong
- Department of Statistics, University of Georgia, 310 Herty Dr., Athens, Georgia 30602, U.S.A
| | - Ping Ma
- Department of Statistics, University of Georgia, 310 Herty Dr., Athens, Georgia 30602, U.S.A
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Xu D, Wang Y. Divide and Recombine Approaches for Fitting Smoothing Spline Models with Large Datasets. J Comput Graph Stat 2018. [DOI: 10.1080/10618600.2017.1402775] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Danqing Xu
- Department of Statistics and Applied Probability, University of California-Santa Barbara, Santa Barbara, CA
| | - Yuedong Wang
- Department of Statistics and Applied Probability, University of California-Santa Barbara, Santa Barbara, CA
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Avery M, Wu Y, Helen Zhang H, Zhang J. RKHS-based functional nonparametric regression for sparse and irregular longitudinal data. CAN J STAT 2014. [DOI: 10.1002/cjs.11215] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Matthew Avery
- Operational Evaluation Division; Institute for Defense Analyses; Alexandria VA USA
| | - Yichao Wu
- Department of Statistics; North Carolina State University; Raleigh NC USA
| | - Hao Helen Zhang
- Department of Mathematics; University of Arizona; Tucson AZ USA
| | - Jiajia Zhang
- Department of Epidemiology and Biostatistics; University of South Carolina Columbia; SC USA
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PRESEE: an MDL/MML algorithm to time-series stream segmenting. ScientificWorldJournal 2013; 2013:386180. [PMID: 23956693 PMCID: PMC3706014 DOI: 10.1155/2013/386180] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2013] [Accepted: 05/09/2013] [Indexed: 11/22/2022] Open
Abstract
Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.
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Sklar JC, Wu J, Meiring W, Wang Y. Nonparametric Regression With Basis Selection From Multiple Libraries. Technometrics 2013. [DOI: 10.1080/00401706.2012.739104] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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CUI XIA, PENG HENG, WEN SONGQIAO, ZHU LIXING. Component Selection in the Additive Regression Model. Scand Stat Theory Appl 2013. [DOI: 10.1111/j.1467-9469.2012.00823.x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Abstract
We propose a nested Gaussian process (nGP) as a locally adaptive prior for Bayesian nonparametric regression. Specified through a set of stochastic differential equations (SDEs), the nGP imposes a Gaussian process prior for the function's mth-order derivative. The nesting comes in through including a local instantaneous mean function, which is drawn from another Gaussian process inducing adaptivity to locally-varying smoothness. We discuss the support of the nGP prior in terms of the closure of a reproducing kernel Hilbert space, and consider theoretical properties of the posterior. The posterior mean under the nGP prior is shown to be equivalent to the minimizer of a nested penalized sum-of-squares involving penalties for both the global and local roughness of the function. Using highly-efficient Markov chain Monte Carlo for posterior inference, the proposed method performs well in simulation studies compared to several alternatives, and is scalable to massive data, illustrated through a proteomics application.
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Affiliation(s)
- Bin Zhu
- Tenure-Track Principal Investigator, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20852
| | - David B Dunson
- Arts & Sciences Distinguished Professor, Department of Statistical Science, Duke University, Durham, NC 27708
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Urbas AA, Choquette SJ. Automated spectral smoothing with spatially adaptive penalized least squares. APPLIED SPECTROSCOPY 2011; 65:665-677. [PMID: 21639989 DOI: 10.1366/10-05971] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
A variety of data smoothing techniques exist to address the issue of noise in spectroscopic data. The vast majority, however, require parameter specification by a knowledgeable user, which is typically accomplished by trial and error. In most situations, optimized parameters represent a compromise between noise reduction and signal preservation. In this work, we demonstrate a nonparametric regression approach to spectral smoothing using a spatially adaptive penalized least squares (SAPLS) approach. An iterative optimization procedure is employed that permits gradual flexibility in the smooth fit when statistically significant trends based on multiscale statistics assuming white Gaussian noise are detected. With an estimate of the noise level in the spectrum the procedure is fully automatic with a specified confidence level for the statistics. Potential application to the heteroscedastic noise case is also demonstrated. Performance was assessed in simulations conducted on several synthetic spectra using traditional error measures as well as comparisons of local extrema in the resulting smoothed signals to those in the true spectra. For the simulated spectra, a best case comparison with the Savitzky-Golay smoothing via an exhaustive parameter search was performed while the SAPLS method was assessed for automated application. The application to several dissimilar experimentally obtained Raman spectra is also presented.
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Affiliation(s)
- Aaron A Urbas
- Biochemical Science Division, Chemical Science and Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8395, USA. aaron.urbas@ nist.gov
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Kim YJ. Semiparametric analysis for case-control studies: a partial smoothing spline approach. J Appl Stat 2010. [DOI: 10.1080/02664760903008979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Sangalli LM, Secchi P, Vantini S, Veneziani A. Efficient estimation of three-dimensional curves and their derivatives by free-knot regression splines, applied to the analysis of inner carotid artery centrelines. J R Stat Soc Ser C Appl Stat 2009. [DOI: 10.1111/j.1467-9876.2008.00653.x] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Abstract
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology - thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
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Affiliation(s)
- David Ruppert
- School of Operations Research and Information Engineering, Cornell University, 1170 Comstock Hall, Ithaca, NY 14853, U.S.A
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Dias R, Ferreira CS, Garcia NL. Penalized maximum likelihood estimation for a function of the intensity of a Poisson point process. STATISTICAL INFERENCE FOR STOCHASTIC PROCESSES 2007. [DOI: 10.1007/s11203-006-9005-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Kim YJ, Gu C. Smoothing spline Gaussian regression: more scalable computation via efficient approximation. J R Stat Soc Series B Stat Methodol 2004. [DOI: 10.1046/j.1369-7412.2003.05316.x] [Citation(s) in RCA: 189] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Wahba G. Soft and hard classification by reproducing kernel Hilbert space methods. Proc Natl Acad Sci U S A 2002; 99:16524-30. [PMID: 12477931 PMCID: PMC139177 DOI: 10.1073/pnas.242574899] [Citation(s) in RCA: 58] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Reproducing kernel Hilbert space (RKHS) methods provide a unified context for solving a wide variety of statistical modelling and function estimation problems. We consider two such problems: We are given a training set [yi, ti, i = 1, em leader, n], where yi is the response for the ith subject, and ti is a vector of attributes for this subject. The value of y(i) is a label that indicates which category it came from. For the first problem, we wish to build a model from the training set that assigns to each t in an attribute domain of interest an estimate of the probability pj(t) that a (future) subject with attribute vector t is in category j. The second problem is in some sense less ambitious; it is to build a model that assigns to each t a label, which classifies a future subject with that t into one of the categories or possibly "none of the above." The approach to the first of these two problems discussed here is a special case of what is known as penalized likelihood estimation. The approach to the second problem is known as the support vector machine. We also note some alternate but closely related approaches to the second problem. These approaches are all obtained as solutions to optimization problems in RKHS. Many other problems, in particular the solution of ill-posed inverse problems, can be obtained as solutions to optimization problems in RKHS and are mentioned in passing. We caution the reader that although a large literature exists in all of these topics, in this inaugural article we are selectively highlighting work of the author, former students, and other collaborators.
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Affiliation(s)
- Grace Wahba
- Department of Statistics, University of Wisconsin, 1210 West Dayton Street, Madison, WI 53706, USA.
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Abstract
Gaussian processes have been widely applied to regression problems with good performance. However, they can be computationally expensive. In order to reduce the computational cost, there have been recent studies on using sparse approximations in gaussian processes. In this article, we investigate properties of certain sparse regression algorithms that approximately solve a gaussian process. We obtain approximation bounds and compare our results with related methods.
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Affiliation(s)
- Tong Zhang
- IBM T J Watson Research Center, Yorktown Heights, NY 10598, USA.
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Gu C, Kim YJ. Penalized likelihood regression: General formulation and efficient approximation. CAN J STAT 2002. [DOI: 10.2307/3316100] [Citation(s) in RCA: 53] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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