Relevant bibliographies by topics / Non-parametric estimation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Non-parametric estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Non-parametric estimation"

1

Adhya, Sumanta. "Bootstrap Variance Estimation for Semiparametric Finite Population Distribution Function Estimator." Calcutta Statistical Association Bulletin 70, no. 1 (2018): 17–32. http://dx.doi.org/10.1177/0008068318765583.

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Estimating finite population distribution function (FPDF) emerges as an important problem to the survey statisticians since the pioneering work of Chambers and Dunstan [1] . It unifies estimation of standard finite population parameters, namely, mean and quantiles. Regarding this, estimating variance of FPDF estimator is an important task for accessing the quality of the estimtor and drawing inferences (e.g., confidence interval estimation) on finite population parameters. Due to non-linearity of FPDF estimator, resampling-based methods are developed earlier for parametric or non-parametric Ch
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Elston, D. A., C. A. Glasbey, and D. R. Neilson. "Non-parametric lactation curves." Animal Science 48, no. 2 (1989): 331–39. http://dx.doi.org/10.1017/s0003356100040320.

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ABSTRACTLactation curves are fitted to data as a preliminary to estimating summary statistics. Two widely quoted curves are atbe-ct (Wood, 1967) and a(1 - e-bt) - ct (Cobby and Le Du, 1978), each of which has three parameters. Restriction to either of these curves imposes limitations on the fit to the data and can result in biased estimation of summary statistics. Alternatively, lactation curves can be generated by the use of a non-parametric method which requires only weak assumptions about the signs of derivatives of the curves. Because the non-parametric curves are more flexible, estimates
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Jadhav, Deepak, and T. V. Ramanathan. "Parametric and non-parametric estimation of value-at-risk." Journal of Risk Model Validation 3, no. 1 (2009): 51–71. http://dx.doi.org/10.21314/jrmv.2009.034.

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Wang, Yong, and Chew-Seng Chee. "Density estimation using non-parametric and semi-parametric mixtures." Statistical Modelling: An International Journal 12, no. 1 (2012): 67–92. http://dx.doi.org/10.1177/1471082x1001200104.

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Bayram, Mustafa, Buyukoz Orucova, and Tugcem Partal. "Parameter estimation in a Black Scholes." Thermal Science 22, Suppl. 1 (2018): 117–22. http://dx.doi.org/10.2298/tsci170915277b.

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In this paper we discuss parameter estimation in black scholes model. A non-parametric estimation method and well known maximum likelihood estimator are considered. Our aim is to estimate the unknown parameters for stochastic differential equation with discrete time observation data. In simulation study we compare the non-parametric method with maximum likelihood method using stochastic numerical scheme named with Euler Maruyama.
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Zhao, Zhibiao, Yiyun Zhang, and Runze Li. "NON-PARAMETRIC ESTIMATION UNDER STRONG DEPENDENCE." Journal of Time Series Analysis 35, no. 1 (2013): 4–15. http://dx.doi.org/10.1111/jtsa.12044.

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Sachs, Rainer von. "PEAK-INSENSITIVE NON-PARAMETRIC SPECTRUM ESTIMATION." Journal of Time Series Analysis 15, no. 4 (1994): 429–52. http://dx.doi.org/10.1111/j.1467-9892.1994.tb00203.x.

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Ullah, Aman. "Non-Parametric Estimation of Econometric Functionals." Canadian Journal of Economics 21, no. 3 (1988): 625. http://dx.doi.org/10.2307/135443.

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Giese, Nadine, Thijs van der Hulst, Paolo Serra, and Tom Oosterloo. "Non-parametric estimation of morphological lopsidedness." Monthly Notices of the Royal Astronomical Society 461, no. 2 (2016): 1656–73. http://dx.doi.org/10.1093/mnras/stw1426.

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Randal 3, John A., Peter J. Thomson, and Martin T. Lally. "Non-parametric estimation of historical volatility." Quantitative Finance 4, no. 4 (2004): 427–40. http://dx.doi.org/10.1080/14697680400008692.

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Dissertations / Theses on the topic "Non-parametric estimation"

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Nicoloutsopoulos, Dimitrios. "Parametric and Bayesian non-parametric estimation of copulas." Thesis, University College London (University of London), 2005. http://discovery.ucl.ac.uk/1445722/.

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This thesis studies parametric and non-parametric methods of cop ula estimation with special focus on the Archimedean class of copu las. The first part proposes an estimation procedure which is indepen dent of the marginal distributions and performs well for one-parame ter or two-parameter families of copulas, where traditional methods give questionable results especially for small sample sizes. In the sec ond part we follow a Bayesian methodology and represent the copula density as a random piecewise constant, function. Under the presence of some data, we set up a probability distribution ove
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Chen, Xiaoyi. "Parametric and Non-parametric Option Hedging and Estimation Based on Hedging Error Minimization." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1606825135996737.

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Yan, Boping. "Double kernel non-parametric estimation in semiparametric econometric models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ35817.pdf.

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Brunel, Victor-Emmanuel. "Non-parametric estimation of convex bodies and convex polytopes." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2014. http://tel.archives-ouvertes.fr/tel-01066977.

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Dans ce travail, nous nous intéressons à l'estimation d'ensembles convexes dans l'espace Euclidien R^d, en nous penchant sur deux modèles. Dans le premier modèle, nous avons à notre disposition un échantillon de n points aléatoires, indépendants et de même loi, uniforme sur un ensemble convexe inconnu. Le second modèle est un modèle additif de régression, avec bruit sous-gaussien, et dont la fonction de régression est l'indicatrice d'Euler d'un ensemble convexe ici aussi inconnu. Dans le premier modèle, notre objectif est de construire un estimateur du support de la densité des observations, q
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Farouj, Younes. "Structured anisotropic sparsity priors for non-parametric function estimation." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI123/document.

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Le problème d'estimer une fonction de plusieurs variables à partir d'une observation corrompue surgit dans de nombreux domaines d'ingénierie. Par exemple, en imagerie médicale cette tâche a attiré une attention particulière et a, même, motivé l'introduction de nouveaux concepts qui ont trouvé des applications dans de nombreux autres domaines. Cet intérêt est principalement du au fait que l'analyse des données médicales est souvent effectuée dans des conditions difficiles car on doit faire face au bruit, au faible contraste et aux transformations indésirables inhérents aux systèmes d'acquisitio
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Joshi, Niranjan Bhaskar. "Non-parametric probability density function estimation for medical images." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:ebc6af07-770b-4fee-9dc9-5ebbe452a0c1.

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The estimation of probability density functions (PDF) of intensity values plays an important role in medical image analysis. Non-parametric PDF estimation methods have the advantage of generality in their application. The two most popular estimators in image analysis methods to perform the non-parametric PDF estimation task are the histogram and the kernel density estimator. But these popular estimators crucially need to be ‘tuned’ by setting a number of parameters and may be either computationally inefficient or need a large amount of training data. In this thesis, we critically analyse and f
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Brunel, Victor Emmanuel. "Non parametric estimation of convex bodies and convex polytopes." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066146/document.

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Dans ce travail, nous nous intéressons à l'estimation d'ensembles convexes dans l'espace Euclidien $\R^d$, en nous penchant sur deux modèles. Dans le premier modèle, nous avons à notre disposition un échantillon de $n$ points aléatoires, indépendants et de même loi, uniforme sur un ensemble convexe inconnu. Le second modèle est un modèle additif de régression, avec bruit sous-gaussien, et dont la fonction de régression est l'indicatrice d'Euler d'un ensemble convexe ici aussi inconnu. Dans le premier modèle, notre objectif est de construire un estimateur du support de la densité des observatio
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Avramidis, Panagiotis. "Estimation of the volatility function : non-parametric and semiparametric approaches." Thesis, London School of Economics and Political Science (University of London), 2004. http://etheses.lse.ac.uk/1793/.

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We investigate two problems in modelling time series data that exhibit conditional heteroscedasticity. The first part deals with the local maximum likelihood estimation of volatility functions which are in the form of conditional variance functions. The existing estimation procedures yield plausible results. Yet, they often fail to take into account special features of the data at the cost of reduced accuracy of prediction. More precisely, many of the parametric and nonparametric conditional variance models ignore the fact that the error distribution departs significantly from gaussian distrib
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Mattiussi, Vanessa. "Non parametric estimation of high-frequency volatility and correlation dynamics." Thesis, City University London, 2010. http://openaccess.city.ac.uk/12095/.

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This thesis addresses the problem of quantitatively evaluating the temporal dynamics that characterized financial time series. In particular, we perform an accurate analysis of the Fourier estimator, a newly proposed nonparametric methodology to measure ex-post volatility and cross-volatilities as functions of time, when financial assets are observed at different highfrequency levels over the day. The estimator has the peculiar feature to employ the observed data in their original form, therefore exploiting all the available information in the sample. We first show how to considerably improve
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Zhang, Jufen. "Bayesian density estimation and classification of incomplete data using semi-parametric and non parametric models." Thesis, University of Exeter, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426082.

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Books on the topic "Non-parametric estimation"

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Cheng, Russell. Non-Standard Parametric Statistical Inference. Oxford University Press, 2017.

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Lall, Somik V. What are public services worth, and to whom ? non-parametric estimation of capitalization in Pune. World Bank, 2006.

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Sawyer, K. R. Non-parametric estimation & testing of econometric models of duration: An application to the Australian longitudinal survey. University of Melbourne,Dept. of Economics, 1988.

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Wheelock, David C. Robust non-parametric quantile estimation of efficiency and productivity change in U.S. commercial banking, 1985-2004. Federal Reserve Bank of St. Louis, 2006.

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Wheelock, David C. Non-parametric, unconditional quantile estimation for efficiency analysis with an application to Federal Reserve check processing operations. Federal Reserve Bank of St. Louis, 2005.

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Brandt, James M. A parametric cost model for estimating operating and support costs of US Navy (Non-Nuclear) surface ships. Naval Postgraduate School, 1999.

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Lall, Somik V., and Mattias Lundberg. What Are Public Services Worth, And To Whom ? Non-Parametric Estimation Of Capitalization In Pune. The World Bank, 2006. http://dx.doi.org/10.1596/1813-9450-3924.

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Cheng, Russell. Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0001.

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This chapter provides an overview of the book. The book investigates non-standard parametric, mainly continuous univariate estimation problems. The basic difference between standard and non-standard problems is explained in this chapter. The book considers different non-standard problems that can arise. Though some of the problems are advanced, a strong emphasis is placed on providing statistical methods to analyse them that are simple to understand and implement. Maximum likelihood (ML) estimation is the main method used to estimate parameters when fitting parametric models. This chapter outl
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Cheng, Russell. Change-Point Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0011.

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This chapter investigates change-point (hazard rate) probability models for the random survival time in some population of interest. A parametric probability distribution is assumed with parameters to be estimated from a sample of observed survival times. If a change-point parameter, denoted by τ‎, is included to represent the time at which there is a discrete change in hazard rate, then the model is non-standard. The profile log-likelihood, with τ‎ as profiling parameter, has a discontinuous jump at every τ‎ equal to a sampled value, becoming unbounded as τ‎ tends to the largest observation.
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A Parametric Cost Model for Estimating Operating and Support Costs of U. S. Navy (Non-Nuclear) Surface Ships. Storming Media, 1999.

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Book chapters on the topic "Non-parametric estimation"

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Shekhar, Shashi, and Hui Xiong. "Estimation, Non-Parametric." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_370.

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Sahu, Pradip Kumar, Santi Ranjan Pal, and Ajit Kumar Das. "Non-parametric Test." In Estimation and Inferential Statistics. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2514-0_6.

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Chonavel, Thierry. "Non-parametric Spectral Estimation." In Statistical Signal Processing. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0139-0_12.

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Florens, Jean-Pierre. "Non-parametric Models with Instrumental Variables." In Inverse Problems and High-Dimensional Estimation. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19989-9_2.

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Chaudhuri, Arijit, and Sanghamitra Pal. "Estimation of Non-linear Parametric Functions." In Indian Statistical Institute Series. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1418-8_10.

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Mateu, Jorge, and Mehdi Moradi. "Non-parametric Intensity Estimation for Spatial Point Patterns with R." In Flexible Nonparametric Curve Estimation. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-66501-1_6.

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Mekler, Philipp, and Jingshu Sun. "Pharma Tender Processes: Modeling Auction Outcomes." In Quantitative Models in Life Science Business. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11814-2_4.

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AbstractThis chapter summarizes the overall tendering and contracting process in the pharmaceutical industry by providing an overview of the first-sealed price auction theory, auction rules, and drug pricing mechanism of different countries. Comparing procurement systems across Asia, Africa, Europe, and Latin America, the review casts light on various pharmaceutical bidding systems across the world and their impact on drug prices. Then, this review focuses on the empirical estimation of first-price auction models. In terms of model specification, we compare the two most commonly used empirical
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Breckling, Jens, and Luca Dal Dosso. "A Non-parametric Approach to Term Structure Estimation." In Wirtschaftswissenschaftliche Beiträge. Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-46948-0_6.

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Eremeev, Anton, and Colin R. Reeves. "Non-parametric Estimation of Properties of Combinatorial Landscapes." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46004-7_4.

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Girault, Jean-Marc, Frederic Ossant, Abdeldjalil Ouahabi, Christelle Guittet, Denis Kouamé, and Fredéric Patat. "Non-Stationary Parametric Spectral Estimation for Ultrasound Attenuation." In Acoustical Imaging. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8588-0_9.

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Conference papers on the topic "Non-parametric estimation"

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Wang, Jinjing, and Xiaoning Wang. "Application of Non-Parametric Estimation Method in Reliability Estimation." In 2023 14th International Conference on Reliability, Maintainability and Safety (ICRMS). IEEE, 2023. http://dx.doi.org/10.1109/icrms59672.2023.00083.

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Li, Hua, HuaChao Mo, Dongyu Wang, et al. "Wind power interval forecasting based on RF-VMD-LSTM and non-parametric estimation." In 2024 6th International Conference on Energy, Power and Grid (ICEPG). IEEE, 2024. https://doi.org/10.1109/icepg63230.2024.10776010.

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Pistone, Giovanni. "New ideas in non-parametric estimation." In Disordered and complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1358178.

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Neumann, Maxim, Sassan S. Saatchi, Lars M. H. Ulander, and Johan E. S. Fransson. "Parametric and non-parametric forest biomass estimation from PolInSAR data." In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6049154.

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Beck, Johannes, and Christoph Stiller. "Non-parametric lane estimation in urban environments." In 2014 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2014. http://dx.doi.org/10.1109/ivs.2014.6856551.

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Sriperumbudur, Bharath K., Kenji Fukumizu, Arthur Gretton, Bernhard Scholkopf, and Gert R. G. Lanckriet. "Non-parametric estimation of integral probability metrics." In 2010 IEEE International Symposium on Information Theory - ISIT. IEEE, 2010. http://dx.doi.org/10.1109/isit.2010.5513626.

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Hu, Yusuo, Hua Chen, Jian-guang Lou, and Jiang Li. "Distributed Density Estimation Using Non-parametric Statistics." In 27th International Conference on Distributed Computing Systems (ICDCS '07). IEEE, 2007. http://dx.doi.org/10.1109/icdcs.2007.100.

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Corona, Enrique, Brian Nutter, and Sunanda Mitra. "Non-parametric Estimation of Mixture Model Order." In Interpretation (SSIAI). IEEE, 2008. http://dx.doi.org/10.1109/ssiai.2008.4512306.

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Elsner, Jens P., Martin Braun, Leonid Chaichenets, and Friedrich K. Jondral. "On linearly precompressed non-parametric spectrum estimation." In 2009 IEEE/SP 15th Workshop on Statistical Signal Processing (SSP). IEEE, 2009. http://dx.doi.org/10.1109/ssp.2009.5278469.

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Agrez, Dusan, and Tomaz Lusin. "Non-parametric estimation of the phase difference." In 2014 Conference on Precision Electromagnetic Measurements (CPEM 2014). IEEE, 2014. http://dx.doi.org/10.1109/cpem.2014.6898396.

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Reports on the topic "Non-parametric estimation"

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Brooks, Dusty. Non-Parametric Source Term Uncertainty Estimation. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1763581.

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Haan, Wouter J. Den, and Andrew Levin. Inferences from Parametric and Non-Parametric Covariance Matrix Estimation Procedures. National Bureau of Economic Research, 1996. http://dx.doi.org/10.3386/t0195.

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Levit, B. Y. Approximately Integrable Linear Statistical Models in Non-Parametric Estimation. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada227395.

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Bournakis, Ioannis. Estimating Firm-Level Production Functions. Instats Inc., 2024. http://dx.doi.org/10.61700/7ofeop0wspyuj1774.

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This seminar provides comprehensive training in estimating Total Factor Productivity (TFP) at the firm level, focusing on empirical methods and real-world application using software tools. Participants will explore advanced techniques such as non-parametric, parametric, and semi-parametric methods, addressing challenges in production function estimation, and gain skills applicable to economic research and policy analysis.
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Wheelock, David C., and Paul W. Wilson. Robust Non-parametric Quantile Estimation of Efficiency and Productivity Change in U.S. Commercial Banking, 1985-2004. Federal Reserve Bank of St. Louis, 2006. http://dx.doi.org/10.20955/wp.2006.041.

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Wheelock, David C., and Paul W. Wilson. Non-parametric, Unconditional Quantile Estimation for Efficiency Analysis with An Application to Federal Reserve Check Processing Operations. Federal Reserve Bank of St. Louis, 2005. http://dx.doi.org/10.20955/wp.2005.027.

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Bouezmarni, Taoufik, Mohamed Doukali, and Abderrahim Taamouti. Copula-based estimation of health concentration curves with an application to COVID-19. CIRANO, 2022. http://dx.doi.org/10.54932/mtkj3339.

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COVID-19 has created an unprecedented global health crisis that caused millions of infections and deaths worldwide. Many, however, argue that pre-existing social inequalities have led to inequalities in infection and death rates across social classes, with the most-deprived classes are worst hit. In this paper, we derive semi/non-parametric estimators of Health Concentration Curve (HC) that can quantify inequalities in COVID-19 infections and deaths and help identify the social classes that are most at risk of infection and dying from the virus. We express HC in terms of copula function that w
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Mohayai, Tanaz Angelina. A Novel Non-Parametric Density Estimation Approach to Measuring Muon Ionization Cooling and Reverse Emittance Exchange in the MICE Experiment. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1515052.

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Rodríguez, Francisco. Cleaning Up the Kitchen Sink: On the Consequences of the Linearity Assumption for Cross-Country Growth Empirics. Inter-American Development Bank, 2006. http://dx.doi.org/10.18235/0011322.

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Existing work in growth empirics either assumes linearity of the growth function or attempts to capture non-linearities by the addition of a small number of quadratic or multiplicative interaction terms. Under a more generalized failure of linearity or if the functional form taken by the non-linearity is not known ex ante, such an approach is inadequate and will lead to biased and inconsistent OLS and instrumental variables estimators. This paper uses non-parametric and semiparametric methods of estimation to evaluate the relevance of strong non-linearities in commonly used growth data sets. O
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Mariscal, Rodrigo, and Andrew Powell. Forecasting Inflation Risks in Latin America: A Technical Note. Inter-American Development Bank, 2012. http://dx.doi.org/10.18235/0009040.

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There are many sources of inflation forecasts for Latin America. The International Monetary Fund, Latin Focus, the Economist Intelligence Unit and other consulting companies all offer inflation forecasts. However, these sources do not provide any probability measures regarding the risk of inflation. In some cases, Central Banks offer forecast and probability analyses but typically their models are not fully transparent. This technical note attempts to develop a relatively homogeneous set of methodologies and employs them to estimate inflation forecasts, probability distributions for those fore
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