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Relevant bibliographies by topics / Nonparametric and semiparametric additive models

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Academic literature on the topic 'Nonparametric and semiparametric additive models'

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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Journal articles on the topic "Nonparametric and semiparametric additive models"

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Wei, Chuanhua, Ran Yan, and Tao Tao. "Statistical Inference on Semiparametric Spatial Additive Model." Journal of Mathematics Research 12, no. 2 (2020): 1. http://dx.doi.org/10.5539/jmr.v12n2p1.

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There has been a growing interest on using nonparametric and semiparametric modelling techniques for the analysis of spatial data because of their powerfulness in extracting the underlying local patterns in the data. In this study, stimulated by the Boston house price data, we apply a semiparametric spatial additive model to incorporation of spatial e ects in regression models. For this semiparametric model, we develop a linear hypothesis test of parametric coecients as well as a test for the existence of the spatial e ects. For the problem of variable selection, the adaptive Lasso method was

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Robinson, Andrew P., Stephen E. Lane, and Guillaume Thérien. "Fitting forestry models using generalized additive models: a taper model example." Canadian Journal of Forest Research 41, no. 10 (2011): 1909–16. http://dx.doi.org/10.1139/x11-095.

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Nonparametric and semiparametric modelling methods are commonly applied in many fields. However, such methods have not been widely adopted in forestry, other than the most similar neighbour and nearest neighbor methods. Generalized additive modelling is a flexible semiparametric regression method that is useful when model-based prediction is the main goal and the parametric form of the model is unknown and possibly complex. Routines to fit generalized additive models (GAMs) are now readily available in much statistical software, making them an attractive option for forest modelling. Here, the

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Wei, Chuanhua, and Xiaonan Wang. "Principal Components Regression Estimation in Semiparametric Partially Linear Additive Models." International Journal of Statistics and Probability 5, no. 1 (2015): 46. http://dx.doi.org/10.5539/ijsp.v5n1p46.

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<p>Partially linear additive model is useful in statistical modelling as a multivariate nonparametric fitting technique. This paper considers statistical inference for the semiparametric model in the presence of multicollinearity. Based on the profile least-squares approach, we propose a novel principal components regression estimator for the parametric component, and provide the asymptotic bias and covariance matrix of the proposed estimator. Some simulations are conducted to examine the performance of our proposed estimators and the results are satisfactory.</p>

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Xie, Xianhong, Howard D. Strickler, and Xiaonan Xue. "Additive Hazard Regression Models: An Application to the Natural History of Human Papillomavirus." Computational and Mathematical Methods in Medicine 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/796270.

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There are several statistical methods for time-to-event analysis, among which is the Cox proportional hazards model that is most commonly used. However, when the absolute change in risk, instead of the risk ratio, is of primary interest or when the proportional hazard assumption for the Cox proportional hazards model is violated, an additive hazard regression model may be more appropriate. In this paper, we give an overview of this approach and then apply a semiparametric as well as a nonparametric additive model to a data set from a study of the natural history of human papillomavirus (HPV) i

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Liu, Ruixuan, and Zhengfei Yu. "Accelerated failure time models with log-concave errors." Econometrics Journal 23, no. 2 (2019): 251–68. http://dx.doi.org/10.1093/ectj/utz024.

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Summary We study accelerated failure time models in which the survivor function of the additive error term is log-concave. The log-concavity assumption covers large families of commonly used distributions and also represents the aging or wear-out phenomenon of the baseline duration. For right-censored failure time data, we construct semiparametric maximum likelihood estimates of the finite-dimensional parameter and establish the large sample properties. The shape restriction is incorporated via a nonparametric maximum likelihood estimator of the hazard function. Our approach guarantees the uni

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Du, Pang, Guang Cheng, and Hua Liang. "Semiparametric regression models with additive nonparametric components and high dimensional parametric components." Computational Statistics & Data Analysis 56, no. 6 (2012): 2006–17. http://dx.doi.org/10.1016/j.csda.2011.12.007.

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Christmann, Andreas, and Ding-Xuan Zhou. "Learning rates for the risk of kernel-based quantile regression estimators in additive models." Analysis and Applications 14, no. 03 (2016): 449–77. http://dx.doi.org/10.1142/s0219530515500050.

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Additive models play an important role in semiparametric statistics. This paper gives learning rates for regularized kernel-based methods for additive models. These learning rates compare favorably in particular in high dimensions to recent results on optimal learning rates for purely nonparametric regularized kernel-based quantile regression using the Gaussian radial basis function kernel, provided the assumption of an additive model is valid. Additionally, a concrete example is presented to show that a Gaussian function depending only on one variable lies in a reproducing kernel Hilbert spac

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Kangas, Annika, Mari Myllymäki, Terje Gobakken, and Erik Næsset. "Model-assisted forest inventory with parametric, semiparametric, and nonparametric models." Canadian Journal of Forest Research 46, no. 6 (2016): 855–68. http://dx.doi.org/10.1139/cjfr-2015-0504.

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Survey sampling with model-assisted estimation has been gaining popularity in forest inventory recently, as the availability of cheap, good-quality remotely sensed data that can be used as auxiliary information has improved. Most of the studies have been carried out using parametric (linear or nonlinear) models. However, nonparametric and semiparametric models such as k nearest neighbor, kernel, and generalized additive are widely used in forest inventory. The results are usually calculated using the difference estimator (i.e., assuming an external model), even though the models used are based

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Li, Zhuokai, Hai Liu, and Wanzhu Tu. "Model selection in multivariate semiparametric regression." Statistical Methods in Medical Research 27, no. 10 (2017): 3026–38. http://dx.doi.org/10.1177/0962280217690769.

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Variable selection in semiparametric mixed models for longitudinal data remains a challenge, especially in the presence of multiple correlated outcomes. In this paper, we propose a model selection procedure that simultaneously selects fixed and random effects using a maximum penalized likelihood method with the adaptive least absolute shrinkage and selection operator penalty. Through random effects selection, we determine the correlation structure among multiple outcomes and therefore address whether a joint model is necessary. Additionally, we include a bivariate nonparametric component, as a

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Zhang, Chenyang, Chuanhua Wei, and Bailing An. "Stochastic Restricted Estimation in Partially Linear Measurement Error Models." International Journal of Statistics and Probability 7, no. 3 (2018): 66. http://dx.doi.org/10.5539/ijsp.v7n3p66.

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As a generalization of nonparametric regression model, partially linear model has been studied extensively in the last decades. This paper considers estimation of the semiparametric model under the situation that the covariates are measured with additive error in the linear part and some additional stochastic linear restrictions exist on the parametric component. Based on the corrected profile least-squares approach and mixed regression method, we propose a stochastic restricted estimator named the corrected profile mixed estimator for the parametric component, and discuss its statistical prop

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Dissertations / Theses on the topic "Nonparametric and semiparametric additive models"

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Fang, Zaili. "Some Advanced Model Selection Topics for Nonparametric/Semiparametric Models with High-Dimensional Data." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/40090.

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Model and variable selection have attracted considerable attention in areas of application where datasets usually contain thousands of variables. Variable selection is a critical step to reduce the dimension of high dimensional data by eliminating irrelevant variables. The general objective of variable selection is not only to obtain a set of cost-effective predictors selected but also to improve prediction and prediction variance. We have made several contributions to this issue through a range of advanced topics: providing a graphical view of Bayesian Variable Selection (BVS), recovering s

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Sun, Peng. "Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/78189.

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Cortese, Giuliana. "Dynamic models for competing risks and relative survival." Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3427193.

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The thesis concerns regression models related to the competing risks setting in survival analysis and deals with both the case of known specific causes and the case of unknown (even if present) specific causes of the event of interest. In the first part, dealing with events whose specific cause is known, competing risks modelling has been applied to a breast cancer study and some of the dynamic aspects such as time-dependent variables are tackled within the context of the application. The aim of the application was to detect an optimal chemotherapy dosage for different typologies of patie

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Häggström, Jenny. "Selection of smoothing parameters with application in causal inference." Doctoral thesis, Umeå universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-39614.

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This thesis is a contribution to the research area concerned with selection of smoothing parameters in the framework of nonparametric and semiparametric regression. Selection of smoothing parameters is one of the most important issues in this framework and the choice can heavily influence subsequent results. A nonparametric or semiparametric approach is often desirable when large datasets are available since this allow us to make fewer and weaker assumptions as opposed to what is needed in a parametric approach. In the first paper we consider smoothing parameter selection in nonparametric regr

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Torrent, Hudson da Silva. "Estimação não-paramétrica e semi-paramétrica de fronteiras de produção." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2010. http://hdl.handle.net/10183/25786.

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Existe uma grande e crescente literatura sobre especificação e estimação de fronteiras de produção e, portanto, de eficiência de unidades produtivas. Nesta tese, o foco esta sobre modelos de fronteiras determinísticas, os quais são baseados na hipótese de que os dados observados pertencem ao conjunto tecnológico. Dentre os modelos estatísticos e estimadores para fronteiras determinísticas existentes, uma abordagem promissora e a adotada por Martins-Filho e Yao (2007). Esses autores propõem um procedimento de estimação composto por três estágios. Esse estimador e de fácil implementação, visto q

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Zhang, Xiangmin. "Nonconvex selection in nonparametric additive models." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/1523.

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High-dimensional data offers researchers increased ability to find useful factors in predicting a response. However, determination of the most important factors requires careful selection of the explanatory variables. In order to tackle this challenge, much work has been done on single or grouped variable selection under the penalized regression framework. Although the topic of variable selection has been extensively studied under the parametric framework, its applications to more flexible nonparametric models are yet to be explored. In order to implement the variable selection in nonparametri

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Liu, Zhenjuan. "Nonparametric and semiparametric estimation and testing of econometric models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0001/NQ43264.pdf.

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Bech, Katarzyna. "On nonparametric additive error models with discrete regressors." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/389713/.

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This thesis contributes to the literature on nonparametric additive error models with discrete explanatory variables. Although nonparametric methods have become very popular in recent decades, research on the impact of the discreteness of regressors is sparse. Main interest is in an unknown nonparametric conditional mean function in the presence of endogenous explanatory variables. Under endogeneity, the identifying power of the model depends on the number of support points of the discrete instrument relative to that of the regressor. Under non-parametric identification failure, we show that s

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Jacho-Chavez, David Tomas. "Identification, estimation and efficiency of nonparametric and semiparametric models in microeconometrics." Thesis, London School of Economics and Political Science (University of London), 2006. http://etheses.lse.ac.uk/1888/.

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The focal point of this thesis is on identification and estimation of nonparametric models, as well as the efficiency and higher order properties of a class of semiparametric estimators in Microeconometrics. We present a new identification result for a particular nonparametric model that nests many popular parametric/nonparametric Econometric models as special cases. Estimators are proposed and their asymptotic properties derived; in particular, they are shown to be consistent and asymptotically pointwise normally distributed. We implement these estimators for the nonparametric estimation and

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Li, Dan. "Bayesian Nonparametric and Semiparametric Models for Categorical, Survival and Longitudinal Data." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1467126804.

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Books on the topic "Nonparametric and semiparametric additive models"

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Härdle, Wolfgang, Axel Werwatz, Marlene Müller, and Stefan Sperlich. Nonparametric and Semiparametric Models. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17146-8.

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Wolfgang, Härdle, ed. Nonparametric and semiparametric models. Springer, 2004.

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Buja, Andreas. Linear smoothers and additive models. University of Toronto, Dept. of Statistics, 1987.

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Estimation in semiparametric models: Some recent developments. Springer-Verlag, 1990.

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Bagdonavičius, V. Additive and multiplicative semiparametric models in accelerated life testing and survival analysis. Queen's University, 1998.

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Ferraty, Frédéric, and Philippe Vieu. A Unifying Classification for Functional Regression Modeling. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.1.

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This article presents a unifying classification for functional regression modeling, and more specifically for modeling the link between two variables X and Y, when the explanatory variable (X) is of a functional nature. It first provides a background on the proposed classification of regression models, focusing on the regression problem and defining parametric, semiparametric, and nonparametric models, and explains how semiparametric modeling can be interpreted in terms of dimension reduction. It then gives four examples of functional regression models, namely: functional linear regression mod

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Härdle, Wolfgang Karl. Nonparametric and Semiparametric Models. Springer, 2012.

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Werwatz, Axel, Marlene Müller, Wolfgang Karl Härdle, and Stefan Sperlich. Nonparametric and Semiparametric Models. Springer London, Limited, 2012.

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Choi, Kyungsoo. Identification and estimation of nonparametric and semiparametric sample selection models. 1992.

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(Editor), Mikhail S. Nikulin, N. Balakrishnan (Editor), M. Mesbah (Editor), and Nikolaos Limnios (Editor), eds. Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life (Statistics for Industry and Technology). Birkhäuser Boston, 2004.

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Book chapters on the topic "Nonparametric and semiparametric additive models"

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Horowitz, Joel L. "Nonparametric Additive Models and Semiparametric Partially Linear Models." In Springer Series in Statistics. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-92870-8_3.

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Hafner, Christian M. "Nonparametric and Semiparametric Models." In Contributions to Economics. Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-662-12605-9_5.

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Hanagal, David D. "Nonparametric and Semiparametric Models." In Modeling Survival Data Using Frailty Models. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1181-3_3.

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Müller, Peter, and Gary Rosner. "Semiparametric PK/PD Models." In Practical Nonparametric and Semiparametric Bayesian Statistics. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1732-9_18.

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Escobar, Michael D., and Mike West. "Computing Nonparametric Hierarchical Models." In Practical Nonparametric and Semiparametric Bayesian Statistics. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1732-9_1.

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Ullah, Aman. "Nonparametric Estimation and Hypothesis Testing in Econometric Models." In Semiparametric and Nonparametric Econometrics. Physica-Verlag HD, 1989. http://dx.doi.org/10.1007/978-3-642-51848-5_7.

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Horowitz, Joel L. "The Asymptotic Efficiency of Semiparametric Estimators for Censored Linear Regression Models." In Semiparametric and Nonparametric Econometrics. Physica-Verlag HD, 1989. http://dx.doi.org/10.1007/978-3-642-51848-5_1.

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Bertail, Patrice. "Empirical Likelihood in Nonparametric and Semiparametric Models." In Statistics for Industry and Technology. Birkhäuser Boston, 2004. http://dx.doi.org/10.1007/978-0-8176-8206-4_19.

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Ibrahim, Joseph G., and Kenneth P. Kleinman. "Semiparametric Bayesian Methods for Random Effects Models." In Practical Nonparametric and Semiparametric Bayesian Statistics. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1732-9_5.

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Lee, Myoung-jae. "Nonparametric Regression." In Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2550-6_8.

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Conference papers on the topic "Nonparametric and semiparametric additive models"

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Rifada, Marisa, Nur Chamidah, and Ratih Ardiati Ningrum. "Estimation of nonparametric ordinal logistic regression model using generalized additive models (GAM) method based on local scoring algorithm." In THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS AND SCIENCES (THE 3RD ICMSc): A Brighter Future with Tropical Innovation in the Application of Industry 4.0. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0111771.

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Reports on the topic "Nonparametric and semiparametric additive models"

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Horowitz, Joel L. Nonparametric additive models. Institute for Fiscal Studies, 2012. http://dx.doi.org/10.1920/wp.cem.2012.2012.

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Lee, Sokbae (Simon), Xiaohong Chen, Victor Chernozhukov, and Whitney K. Newey. Local identification of nonparametric and semiparametric models. Institute for Fiscal Studies, 2011. http://dx.doi.org/10.1920/wp.cem.2011.1711.

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Chen, Xiaohong, Sokbae (Simon) Lee, Victor Chernozhukov, and Whitney K. Newey. Local identification of nonparametric and semiparametric models. Institute for Fiscal Studies, 2012. http://dx.doi.org/10.1920/wp.cem.2012.3712.

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Hu, Yingyao, and Susanne M. Schennach. Nonparametric identification and semiparametric estimation of classical measurement error models without side information. Cemmap, 2012. http://dx.doi.org/10.1920/wp.cem.2012.4012.

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Dong, Chaohua, and Oliver Linton. Additive nonparametric models with time variable and both stationary and nonstationary regressions. The IFS, 2017. http://dx.doi.org/10.1920/wp.cem.2017.5917.

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Hauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202305.

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In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference us

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