Relevant bibliographies by topics / Nonparametric smoothing method

Academic literature on the topic 'Nonparametric smoothing method'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Nonparametric smoothing method"

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Takezawa, K. "Use of nonparametric smoothing method for growth analysis." Japanese Journal of Biometrics 9, no. 1 (1988): 11–18. http://dx.doi.org/10.5691/jjb.9.11.

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Kuželka, K., and R. Marušák. "Use of nonparametric regression methods for developing a local stem form model." Journal of Forest Science 60, No. 11 (November 14, 2014): 464–71. http://dx.doi.org/10.17221/56/2014-jfs.

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A local mean stem curve of spruce was represented using regression splines. Abilities of smoothing spline and P-spline to model the mean stem curve were evaluated using data of 85 carefully measured stems of Norway spruce. For both techniques the optimal amount of smoothing was investigated in dependence on the number of training stems using a cross-validation method. Representatives of main groups of parametric models – single models, segmented models and models with variable coefficient – were compared with spline models using five statistic criteria. Both regression splines performed comparably or better as all representatives of parametric models independently of the numbers of stems used as training data.  
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Mahmoud, Hamdy F. F. "Parametric Versus Semi and Nonparametric Regression Models." International Journal of Statistics and Probability 10, no. 2 (February 23, 2021): 90. http://dx.doi.org/10.5539/ijsp.v10n2p90.

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There are three common types of regression models: parametric, semiparametric and nonparametric regression. The model should be used to fit the real data depends on how much information is available about the form of the relationship between the response variable and explanatory variables, and the random error distribution that is assumed. Researchers need to be familiar with each modeling approach requirements. In this paper, differences between these models, common estimation methods, robust estimation, and applications are introduced. For parametric models, there are many known methods of estimation, such as least squares and maximum likelihood methods which are extensively studied but they require strong assumptions. On the other hand, nonparametric regression models are free of assumptions regarding the form of the response-explanatory variables relationships but estimation methods, such as kernel and spline smoothing are computationally expensive and smoothing parameters need to be obtained. For kernel smoothing there two common estimators: local constant and local linear smoothing methods. In terms of bias, especially at the boundaries of the data range, local linear is better than local constant estimator.  Robust estimation methods for linear models are well studied, however the robust estimation methods in nonparametric regression methods are limited. A robust estimation method for the semiparametric and nonparametric regression models is introduced.
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Zheng, Xu. "Testing parametric conditional distributions using the nonparametric smoothing method." Metrika 75, no. 4 (November 23, 2010): 455–69. http://dx.doi.org/10.1007/s00184-010-0336-2.

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You, Wei Zhen, and Xiao Pin Zhong. "Modeling System Reliability Using a Nonparametric Method." Applied Mechanics and Materials 687-691 (November 2014): 1193–97. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1193.

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System reliability is an important problem especially in reliability engineering. The frequency a system failure happens is represented by failure rate. We use failure rate instead of system reliability to analyze a particular system.Traditional parametric models cannot give a good fit to complex systems, wetherefore employed a nonparametric method in this paper. Gaussian smoothing is also applied on the failure rate curves. Compared with parametric models, the nonparametric model yields more accurateestimation of system failure rate.
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Rezapour, Mahdi, and Khaled Ksaibati. "Semi and Nonparametric Conditional Probability Density, a Case Study of Pedestrian Crashes." Open Transportation Journal 15, no. 1 (December 31, 2021): 280–88. http://dx.doi.org/10.2174/1874447802115010280.

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Background: Kernel-based methods have gained popularity as employed model residual’s distribution might not be defined by any classical parametric distribution. Kernel-based method has been extended to estimate conditional densities instead of conditional distributions when data incorporate both discrete and continuous attributes. The method often has been based on smoothing parameters to use optimal values for various attributes. Thus, in case of an explanatory variable being independent of the dependent variable, that attribute would be dropped in the nonparametric method by assigning a large smoothing parameter, giving them uniform distributions so their variances to the model’s variance would be minimal. Objectives: The objective of this study was to identify factors to the severity of pedestrian crashes based on an unbiased method. Especially, this study was conducted to evaluate the applicability of kernel-based techniques of semi- and nonparametric methods on the crash dataset by means of confusion techniques. Methods: In this study, two non- and semi-parametric kernel-based methods were implemented to model the severity of pedestrian crashes. The estimation of the semi-parametric densities is based on the adoptive local smoothing and maximization of the quasi-likelihood function, which is similar somehow to the likelihood of the binary logit model. On the other hand, the nonparametric method is based on the selection of optimal smoothing parameters in estimation of the conditional probability density function to minimize mean integrated squared error (MISE). The performances of those models are evaluated by their prediction power. To have a benchmark for comparison, the standard logistic regression was also employed. Although those methods have been employed in other fields, this is one of the earliest studies that employed those techniques in the context of traffic safety. Results: The results highlighted that the nonparametric kernel-based method outperforms the semi-parametric (single-index model) and the standard logit model based on the confusion matrices. To have a vision about the bandwidth selection method for removal of the irrelevant attributes in nonparametric approach, we added some noisy predictors to the models and a comparison was made. Extensive discussion has been made in the content of this study regarding the methodological approach of the models. Conclusion: To summarize, alcohol and drug involvement, driving on non-level grade, and bad lighting conditions are some of the factors that increase the likelihood of pedestrian crash severity. This is one of the earliest studies that implemented the methods in the context of transportation problems. The nonparametric method is especially recommended to be used in the field of traffic safety when there are uncertainties regarding the importance of predictors as the technique would automatically drop unimportant predictors.
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Kaushanskiy, Vadim, and Victor Lapshin. "A nonparametric method for term structure fitting with automatic smoothing." Applied Economics 48, no. 58 (May 21, 2016): 5654–66. http://dx.doi.org/10.1080/00036846.2016.1181835.

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OSMANI, El Hassene, Mounir Haddou, Naceurdine Bensalem, and Lina Abdallah. "A new smoothing method for nonlinear complementarity problems involving P0-function." Statistics, Optimization & Information Computing 10, no. 4 (September 29, 2022): 1267–92. http://dx.doi.org/10.19139/soic-2310-5070-1493.

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In this paper, we present a family of smoothing methods to solve nonlinear complementarity problems (NCPs) involving P0-function. Several regularization or approximation techniques like Fisher-Burmeister’s method, interior-point methods (IPMs) approaches, or smoothing methods already exist. All the corresponding methods solve a sequence of nonlinear systems of equations and depend on parameters that are difficult to drive to zero. The main novelty of our approach is to consider the smoothing parameters as variables that converge by themselves to zero. We do not need any complicated updating strategy, and then obtain nonparametric algorithms. We prove some global and local convergence results and present several numerical experiments, comparisons, and applications that show the efficiency of our approach.
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Jayanti, Putu Gita Karlina, Rahma Anisa, Muhammad Nur Aidi, and Erfiani. "Penerapan Teknik Prapemrosesan Smoothing Spline pada Data Hasil Pengukuran Alat Pemantau Kadar Glukosa Darah Non-Invasif." Xplore: Journal of Statistics 2, no. 2 (August 31, 2018): 15–23. http://dx.doi.org/10.29244/xplore.v2i2.90.

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A non-invasive blood glucose monitoring device is performed without injuring the limbs. One method of measurement in the form of qualitative and relatively simple to use because the process is fast and requires a cheap cost, namely Fourier Transform Infrared (FTIR). Spectroscopic results allow for a shifting of the scatter, since the same object is measured several times incorrectly producing the same spectrum, requiring a preprocessing method to reduce the problem. However, in some cases it is difficult to identify the existing data pattern, so that a nonparametric approach is needed to identify the pattern of data held so that in the process of calibration model obtained accurate results. Smoothing Spline is one nonparametric method is piecewise polynomial, which is a piece of polynomial that has a segmented property on the hose k that formed at knot points, thus providing flexibility in constructing the shape of the curve that we have. The Smoothing Spline method produces an optimum value when the GCV value is minimum on the use of a linear order with sixteen knot points. The resulting varians value after Smoothing Spline method is smaller than before smoothing, this indicates that this method can minimize the effect of liquefaction in the non-invasive blood glucose value spectrum. In addition, Smoothing Spline method can also capture data patterns well.
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Ampa, Andi Tenri, I. Nyoman Budiantara, and Ismaini Zain. "Selection of Optimal Smoothing Parameters in Mixed Estimator of Kernel and Fourier Series in Semiparametric Regression." Journal of Physics: Conference Series 2123, no. 1 (November 1, 2021): 012035. http://dx.doi.org/10.1088/1742-6596/2123/1/012035.

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Abstract In this article, we propose a new method of selecting smoothing parameters in semiparametric regression. This method is used in semiparametric regression estimation where the nonparametric component is partially approximated by multivariable Fourier Series and partly approached by multivariable Kernel. Selection of smoothing parameters using the method with Generalized Cross-Validation (GCV). To see the performance of this method, it is then applied to the data drinking water quality sourced from Regional Drinking Water Company (PDAM) Surabaya by using Fourier Series with trend and Gaussian Kernel. The results showed that this method contributed a good performance in selecting the optimal smoothing parameters.
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Dissertations / Theses on the topic "Nonparametric smoothing method"

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Amezziane, Mohamed. "SMOOTHING PARAMETER SELECTION IN NONPARAMETRIC FUNCTIONAL ESTIMATION." Doctoral diss., University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3488.

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This study intends to build up new techniques for how to obtain completely data-driven choices of the smoothing parameter in functional estimation, within the confines of minimal assumptions. The focus of the study will be within the framework of the estimation of the distribution function, the density function and their multivariable extensions along with some of their functionals such as the location and the integrated squared derivatives.
Ph.D.
Department of Mathematics
Arts and Sciences
Mathematics
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Chen, Tianlei. "Cure Rate Models with Nonparametric Form of Covariate Effects." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/52894.

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This thesis focuses on development of spline-based hazard estimation models for cure rate data. Such data can be found in survival studies with long term survivors. Consequently, the population consists of the susceptible and non-susceptible sub-populations with the latter termed as "cured". The modeling of both the cure probability and the hazard function of the susceptible sub-population is of practical interest. Here we propose two smoothing-splines based models falling respectively into the popular classes of two component mixture cure rate models and promotion time cure rate models. Under the framework of two component mixture cure rate model, Wang, Du and Liang (2012) have developed a nonparametric model where the covariate effects on both the cure probability and the hazard component are estimated by smoothing splines. Our first development falls under the same framework but estimates the hazard component based on the accelerated failure time model, instead of the proportional hazards model in Wang, Du and Liang (2012). Our new model has better interpretation in practice. The promotion time cure rate model, motivated from a simplified biological interpretation of cancer metastasis, was first proposed only a few decades ago. Nonetheless, it has quickly become a competitor to the mixture models. Our second development aims to provide a nonparametric alternative to the existing parametric or semiparametric promotion time models.
Ph. D.
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Chen, Jinsong. "Variance analysis for kernel smoothing of a varying-coefficient model with longitudinal data /." Electronic version (PDF), 2003. http://dl.uncw.edu/etd/2003/chenj/jinsongchen.pdf.

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Kourabi, Mohammad Fawaz [Verfasser], and Jürgen [Akademischer Betreuer] Franke. "Local Smoothing Methods with Regularization in Nonparametric Regression Models / Mohammad Fawaz Kourabi. Betreuer: Jürgen Franke." Kaiserslautern : Universitätsbibliothek Kaiserslautern, 2011. http://d-nb.info/1014529034/34.

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Ma, Hon Wai. "Smoothing locally regular processes by Bayesian nonparametric methods, with applications to acid rain data analysis." Thesis, University of British Columbia, 1986. http://hdl.handle.net/2429/26004.

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We consider the problem of recovering an unknown smooth function from the data using the Bayesian nonparametric approach proposed by Weerahandi and Zidek (1985). Selected nonparametric smoothing methods are reviewed and compared with this new method. At each value of the independent variable, the smooth function is assumed to be expandable in a Taylor series to the pth order. Two methods, cross-validation and "backfitting" are used to estimate the a priori unspecified hyperparameters. Moreover, a data-based procedure is introduced to select the appropriate order p. Finally, an analysis of an acid-rain, wet-deposition time series is included to indicate the efficacy of the proposed methods.
Science, Faculty of
Statistics, Department of
Graduate
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Morell, Oliver [Verfasser], Roland [Akademischer Betreuer] Fried, and Christine H. [Akademischer Betreuer] Müller. "On nonparametric methods for robust jump preserving smoothing and trend detection / Oliver Morell. Betreuer: Roland Fried. Gutachter: Christine H. Müller." Dortmund : Universitätsbibliothek Dortmund, 2012. http://d-nb.info/1100165924/34.

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Rosales, Marticorena Luis Francisco. "Empirical Bayesian Smoothing Splines for Signals with Correlated Errors: Methods and Applications." Doctoral thesis, 2016. http://hdl.handle.net/11858/00-1735-0000-0028-87F9-6.

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Books on the topic "Nonparametric smoothing method"

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Racine, Jean. A nonparametric variable kernel method for local adaptive smoothing of regression functions and associated response coefficients. Toronto, Ont: Dept. of Economics, York University, 1991.

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Tibshirani, Robert. Smoothing methods for the study of synergism. Toronto: University of Toronto, Dept. of Statistics, 1989.

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Hart, Jeffrey D. Nonparametric smoothing and lack-of-fit tests. New York: Springer, 1997.

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Cai, Zongwu. Functional Coefficient Models for Economic and Financial Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.6.

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This article discusses the use of functional coefficient models for economic and financial data analysis. It first provides an overview of recent developments in the nonparametric estimation and testing of functional coefficient models, with particular emphasis on the kernel local polynomial smoothing method, before considering misspecification testing as an important econometric question when fitting a functional (varying) coefficient model or a trending time-varying coefficient model. It then describes two major real-life applications of functional coefficient models in economics and finance: the first deals with the use of functional coefficient instrumental-variable models to investigate the empirical relation between wages and education in a random sample of young Australian female workers from the 1985 wave of the Australian Longitudinal Survey, and the second is concerned with the use of functional coefficient beta models to analyze the common stock price of Microsoft stock (MSFT) during the year 2000 using the daily closing prices.
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McMurry, Timothy, and Dimitris Politis. Resampling methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.7.

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This article examines the current state of methodological and practical developments for resampling inference techniques in functional data analysis, paying special attention to situations where either the data and/or the parameters being estimated take values in a space of functions. It first provides the basic background and notation before discussing bootstrap results from nonparametric smoothing, taking into account confidence bands in density estimation as well as confidence bands in nonparametric regression and autoregression. It then considers the major results in subsampling and what is known about bootstraps, along with a few recent real-data applications of bootstrapping with functional data. Finally, it highlights possible directions for further research and exploration.
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Delsol, Laurent. Nonparametric Methods for α-Mixing Functional Random Variables. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.5.

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This article considers how functional kernel methods can be used to study α-mixing datasets. It first provides an overview of how prediction problems involving dependent functional datasets may arise from the study of time series, focusing on the standard discretized model and modelization that takes into account the functional nature of the evolution of the quantity to be studied over time. It then considers strong mixing conditions, with emphasis on the notion of α-mixing coefficients and α-mixing variables introduced by Rosenblatt (1956). It also describes some conditions for a Markov chain to be α-mixing; some useful tools that provide covariance inequalities, exponential inequalities, and Central Limit Theorem (CLT) for α-mixing sequences; the asymptotic properties of functional kernel estimators; the use of kernel smoothing methods with α-mixing datasets; and various functional kernel estimators corresponding to different prediction methods. Finally, the article highlights some interesting prospects for further research.
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Hart, Jeffrey D. Nonparametric Smoothing and Lack-of-Fit Tests. Springer New York, 2012.

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Ferraty, Frédéric, and Philippe Vieu. Kernel Regression Estimation for Functional Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.4.

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This article provides an overview of recent nonparametric and semiparametric advances in kernel regression estimation for functional data. In particular, it considers the various statistical techniques based on kernel smoothing ideas that have recently been developed for functional regression estimation problems. The article first examines nonparametric functional regression modelling before discussing three popular functional regression estimates constructed by means of kernel ideas, namely: the Nadaraya-Watson convolution kernel estimate, the kNN functional estimate, and the local linear functional estimate. Uniform asymptotic results are then presented. The article proceeds by reviewing kernel methods in semiparametric functional regression such as single functional index regression and partial linear functional regression. It also looks at the use of kernels for additive functional regression and concludes by assessing the impact of kernel methods on practical real-data analysis involving functional (curves) datasets.
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Book chapters on the topic "Nonparametric smoothing method"

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Mammen, Enno. "Resampling Methods for Nonparametric Regression." In Smoothing and Regression, 425–50. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118150658.ch14.

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Moussa, M. A. A. "Nonparametric Regression Methods for Smoothing Curves." In Computational Statistics, 477–80. Heidelberg: Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-662-26811-7_66.

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"Global Smoothing Methods." In Nonparametric Models for Longitudinal Data, 289–330. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/b20631-22.

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"Nonparametric Regression." In Kernel Smoothing: Principles, Methods and Applications, 59–104. Oxford, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118890370.ch2.

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"Basis Approximation Smoothing Methods." In Nonparametric Models for Longitudinal Data, 127–52. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/b20631-16.

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"Penalized Smoothing Spline Methods." In Nonparametric Models for Longitudinal Data, 153–76. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/b20631-17.

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"The One-Step Local Smoothing Methods." In Nonparametric Models for Longitudinal Data, 223–70. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/b20631-20.

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"The Two-Step Local Smoothing Methods." In Nonparametric Models for Longitudinal Data, 271–88. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/b20631-21.

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Aue, Alexander, and Thomas C. M. Lee. "Fast Scatterplot Smoothing Using Blockwise Least Squares Fitting." In Nonparametric Statistical Methods and Related Topics, 299–314. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814366571_0016.

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Linton, O., and E. Mammen. "Nonparametric smoothing methods for a class of non-standard curve estimation problems." In Recent Advances and Trends in Nonparametric Statistics, 203–15. Elsevier, 2003. http://dx.doi.org/10.1016/b978-044451378-6/50014-4.

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Conference papers on the topic "Nonparametric smoothing method"

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Shareghi, Ehsan, Gholamreza Haffari, and Trevor Cohn. "Compressed Nonparametric Language Modelling." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/376.

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Hierarchical Pitman-Yor Process priors are compelling for learning language models, outperforming point-estimate based methods. However, these models remain unpopular due to computational and statistical inference issues, such as memory and time usage, as well as poor mixing of sampler. In this work we propose a novel framework which represents the HPYP model compactly using compressed suffix trees. Then, we develop an efficient approximate inference scheme in this framework that has a much lower memory footprint compared to full HPYP and is fast in the inference time. The experimental results illustrate that our model can be built on significantly larger datasets compared to previous HPYP models, while being several orders of magnitudes smaller, fast for training and inference, and outperforming the perplexity of the state-of-the-art Modified Kneser-Ney count-based LM smoothing by up to 15%.
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