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Relevant bibliographies by topics / Hellinger's distance

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Academic literature on the topic 'Hellinger's distance'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Hellinger's distance"

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McIntyre, Lauren M., and B. S. Weir. "Hardy-Weinberg Testing for Continuous Data." Genetics 147, no. 4 (December 1, 1997): 1965–75. http://dx.doi.org/10.1093/genetics/147.4.1965.

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Abstract Estimation of allelic and genotypic distributions for continuous data using kernel density estimation is discussed and illustrated for some variable number of tandem repeat data. These kernel density estimates provide a useful representation of data when only some of the many variants at a locus are present in a sample. Two Hardy-Weinberg test procedures are introduced for continuous data: a continuous chi-square test with test statistic TCCS and a test based on Hellinger's distance with test statistic TCCS. Simulations are used to compare the powers of these tests to each other and to the powers of a test of intraclass correlation TIC, as well as to the power of Fisher's exact test TFET applied to discretized data. Results indicate that the power of TCCS is better than that of THD but neither is as powerful as TFET. The intraclass correlation test does not perform as well as the other tests examined in this article.

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Wu, Jingjing, and Rohana J. Karunamuni. "Profile Hellinger distance estimation." Statistics 49, no. 4 (August 12, 2014): 711–40. http://dx.doi.org/10.1080/02331888.2014.946928.

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Si-Ma, Ling-Han, Jian Zhang, Bin-Qiang Wang, and Yan-Yu Zhang. "Hellinger-Distance-Optimal Space Constellations." IEEE Communications Letters 21, no. 4 (April 2017): 765–68. http://dx.doi.org/10.1109/lcomm.2017.2650234.

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Wu, Jingjing, and Rohana J. Karunamuni. "On minimum Hellinger distance estimation." Canadian Journal of Statistics 37, no. 4 (December 2009): 514–33. http://dx.doi.org/10.1002/cjs.10042.

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Pitrik, József, and Dániel Virosztek. "Quantum Hellinger distances revisited." Letters in Mathematical Physics 110, no. 8 (March 10, 2020): 2039–52. http://dx.doi.org/10.1007/s11005-020-01282-0.

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Withers, Christopher S., and Saralees Nadarajah. "METHODS FOR SYMMETRIZING RANDOM VARIABLES." Probability in the Engineering and Informational Sciences 24, no. 4 (August 19, 2010): 549–59. http://dx.doi.org/10.1017/s0269964810000173.

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Let X be a random variable with nonsymmetric density p(x). We give the symmetric density q(x) closest to it in the sense of Kulback–Liebler and Hellinger distances. (All symmetries are around zero.) For the first distance, we show that q(x) is proportional to the geometric mean of p(x) and p(−x). For example, a symmetrized shifted exponential is a centered uniform, and a symmetrized shifted gamma is a centered beta random variable. For the second distance, q(x) is proportional to the square of the arithmetic mean of p(x)1/2 and p(−x)1/2. Sample versions are also given for each. We also give the optimal random function f such that f(X) is symmetrically distributed and minimizes |f(X)−X|. Finally, we show how to optimize the Hellinger distance for vector X subject to supersymmetry and for scalar X subject to being monotone about zero in each half-line.

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Kadjo, Roger, Ouagnina Hili, and Aubin N'dri. "Minimum Hellinger Distance Estimation of a Univariate GARCH Process." Journal of Mathematics Research 9, no. 3 (May 28, 2017): 80. http://dx.doi.org/10.5539/jmr.v9n3p80.

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In this paper, we determine the Minimum Hellinger Distance estimator of a stationary GARCH process. We construct an estimator of the parameters based on the minimum Hellinger distance method. Under conditions which ensure the $\phi$-mixing of the GARCH process, we establish the almost sure convergence and the asymptotic normality of the estimator.

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Hili, Ouagnina. "Hellinger distance estimation of SSAR models." Statistics & Probability Letters 53, no. 3 (June 2001): 305–14. http://dx.doi.org/10.1016/s0167-7152(01)00086-4.

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Shemyakin, Arkady. "Hellinger Distance and Non-informative Priors." Bayesian Analysis 9, no. 4 (December 2014): 923–38. http://dx.doi.org/10.1214/14-ba881.

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Kitsos, C., and T. Toulias. "Hellinger Distance Between Generalized Normal Distributions." British Journal of Mathematics & Computer Science 21, no. 2 (January 10, 2017): 1–16. http://dx.doi.org/10.9734/bjmcs/2017/32229.

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Dissertations / Theses on the topic "Hellinger's distance"

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Bissinger, Brett Bose N. K. Culver R. Lee. "Minimum hellinger distance classification of underwater acoustic signals." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4677/index.html.

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Goussakov, Roma. "Hellinger Distance-based Similarity Measures for Recommender Systems." Thesis, Umeå universitet, Statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172385.

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Recommender systems are used in online sales and e-commerce for recommending potential items/products for customers to buy based on their previous buying preferences and related behaviours. Collaborative filtering is a popular computational technique that has been used worldwide for such personalized recommendations. Among two forms of collaborative filtering, neighbourhood and model-based, the neighbourhood-based collaborative filtering is more popular yet relatively simple. It relies on the concept that a certain item might be of interest to a given customer (active user) if, either he appreciated similar items in the buying space, or if the item is appreciated by similar users (neighbours). To implement this concept different kinds of similarity measures are used. This thesis is set to compare different user-based similarity measures along with defining meaningful measures based on Hellinger distance that is a metric in the space of probability distributions. Data from a popular database MovieLens will be used to show the effectiveness of dierent Hellinger distance-based measures compared to other popular measures such as Pearson correlation (PC), cosine similarity, constrained PC and JMSD. The performance of dierent similarity measures will then be evaluated with the help of mean absolute error, root mean squared error and F-score. From the results, no evidence were found to claim that Hellinger distance-based measures performed better than more popular similarity measures for the given dataset.

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Xiang, Sijia. "Minimum Hellinger distance estimation in a semiparametric mixture model." Kansas State University, 2012. http://hdl.handle.net/2097/13762.

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Master of Science
Department of Statistics
Weixin Yao
In this report, we introduce the minimum Hellinger distance (MHD) estimation method and review its history. We examine the use of Hellinger distance to obtain a new efficient and robust estimator for a class of semiparametric mixture models where one component has known distribution while the other component and the mixing proportion are unknown. Such semiparametric mixture models have been used in biology and the sequential clustering algorithm. Our new estimate is based on the MHD, which has been shown to have good efficiency and robustness properties. We use simulation studies to illustrate the finite sample performance of the proposed estimate and compare it to some other existing approaches. Our empirical studies demonstrate that the proposed minimum Hellinger distance estimator (MHDE) works at least as well as some existing estimators for most of the examples considered and outperforms the existing estimators when the data are under contamination. A real data set application is also provided to illustrate the effectiveness of our proposed methodology.

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Dias, Ronaldo 1959. "Estimação por minima distancia de Hellinger." [s.n.], 1988. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307346.

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Orientador : Jose Antonio Cordeiro
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Ciencia da Computação
Made available in DSpace on 2018-07-14T18:46:42Z (GMT). No. of bitstreams: 1 Dias_Ronaldo_M.pdf: 681214 bytes, checksum: d58913b4baf740c4de007fb27ed3257f (MD5) Previous issue date: 1988
Resumo: Não informado
Abstract: Not informed
Mestrado
Mestre em Estatística

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Warwick, Jane. "Selecting tuning parameters in minimum distance estimators." Thesis, Open University, 2002. http://oro.open.ac.uk/19918/.

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Many minimum distance estimators have the potential to provide parameter estimates which are both robust and efficient and yet, despite these highly desirable theoretical properties, they are rarely used in practice. This is because the performance of these estimators is rarely guaranteed per se but obtained by placing a suitable value on some tuning parameter. Hence there is a risk involved in implementing these methods because if the value chosen for the tuning parameter is inappropriate for the data to which the method is applied, the resulting estimators may not have the desired theoretical properties and could even perform less well than one of the simpler, more widely used alternatives. There are currently no data-based methods available for deciding what value one should place on these tuning parameters hence the primary aim of this research is to develop an objective way of selecting values for the tuning parameters in minimum distance estimators so that the full potential of these estimators might be realised. This new method was initially developed to optimise the performance of the density power divergence estimator, which was proposed by Basu, Harris, Hjort and Jones [3]. The results were very promising so the method was then applied to two other minimum distance estimators and the results compared.

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Yan, Huey. "Generalized Minimum Penalized Hellinger Distance Estimation and Generalized Penalized Hellinger Deviance Testing for Generalized Linear Models: The Discrete Case." DigitalCommons@USU, 2001. https://digitalcommons.usu.edu/etd/7066.

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In this dissertation, robust and efficient alternatives to quasi-likelihood estimation and likelihood ratio tests are developed for discrete generalized linear models. The estimation method considered is a penalized minimum Hellinger distance procedure that generalizes a procedure developed by Harris and Basu for estimating parameters of a single discrete probability distribution from a random sample. A bootstrap algorithm is proposed to select the weight of the penalty term. Simulations are carried out to compare the new estimators with quasi-likelihood estimation. The robustness of the estimation procedure is demonstrated by simulation work and by Hapel's α-influence curve. Penalized minimum Hellinger deviance tests for goodness-of-fit and for testing nested linear hypotheses are proposed and simulated. A nonparametric bootstrap algorithm is proposed to obtain critical values for the testing procedure.

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D'Ambrosio, Philip. "A Differential Geometry-Based Algorithm for Solving the Minimum Hellinger Distance Estimator." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32228.

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Robust estimation of statistical parameters is traditionally believed to exist in a trade space between robustness and efficiency. This thesis examines the Minimum Hellinger Distance Estimator (MHDE), which is known to have desirable robustness properties as well as desirable efficiency properties. This thesis confirms that the MHDE is simultaneously robust against outliers and asymptotically efficient in the univariate location case. Robustness results are then extended to the case of simple linear regression, where the MHDE is shown empirically to have a breakdown point of 50%. A geometric algorithm for solution of the MHDE is developed and implemented. The algorithm utilizes the Riemannian manifold properties of the statistical model to achieve an algorithmic speedup. The MHDE is then applied to an illustrative problem in power system state estimation. The power system is modeled as a structured linear regression problem via a linearized direct current model; robustness results in this context have been investigated and future research areas have been identified from both a statistical perspective as well as an algorithm design standpoint.
Master of Science

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Anver, Haneef Mohamed. "Mean Hellinger Distance as an Error Criterion in Univariate and Multivariate Kernel Density Estimation." OpenSIUC, 2010. https://opensiuc.lib.siu.edu/dissertations/161.

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Ever since the pioneering work of Parzen the mean square error( MSE) and its integrated form (MISE) have been used as the error criteria in choosing the bandwidth matrix for multivariate kernel density estimation. More recently other criteria have been advocated as competitors to the MISE, such as the mean absolute error. In this study we define a weighted version of the Hellinger distance for multivariate densities and show that it has an asymptotic form, which is one-fourth the asymptotic MISE under weak smoothness conditions on the multivariate density f. In addition the proposed criteria give rise to a new data-dependent bandwidth matrix selector. The performance of the new data-dependent bandwidth matrix selector is compared with other well known bandwidth matrix selectors such as the least squared cross validation (LSCV) and the plug-in (HPI) through simulation. We derived a closed form formula for the mean Hellinger distance (MHD) in the univariate case. We also compared via simulation mean weighted Hellinger distance (MWHD) and the asymptotic MWHD, and the MISE and the asymptotic MISE for both univariate and bivariate cases for various densities and sample sizes.

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Alexandridis, Roxana Antoanela. "Minimum disparity inference for discrete ranked set sampling data." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1126033164.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also includes graphics. Includes bibliographical references (p. 121-124). Available online via OhioLINK's ETD Center

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Li, Jing. "Digital Signal Characterization for Seizure Detection Using Frequency Domain Analysis." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296861.

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Nowadays, a significant proportion of the population in the world is affected by cerebral diseases like epilepsy. In this study, frequency domain features of electroencephalography (EEG) signals were studied and analyzed, with a view being able to detect epileptic seizures more easily. The power spectrum and spectrogram were determined by using fast fourier transform (FFT) and the scalogram was found by performing continuous wavelet transform (CWT) on the testing EEG signal. In addition, two schemes, i.e. method 1 and method 2, were implemented for detecting epileptic seizures and the applicability of the two methods to electrocardiogram (ECG) signals were tested. A third method for anomaly detection in ECG signals was tested.
En signifikant del av population påverkas idag av neurala sjukdomar som epilepsi. I denna studie studerades och analyserades egenskaper inom frekvensdomänen av elektroencefalografi (EEG), med sikte på att lättare kunna upptäcka epileptiska anfall. Effektspektrumet och spektrogramet bestämdes med hjälp av en snabb fouriertransform och skalogrammet hittades genom att genomföra en kontinuerlig wavelet transform (CWT) på testsignalen från EEGsignalen. I addition till detta skapades två system, metod 1 och metod 2, som implementerades för att upptäcka epileptiska anfall. Användbarheten av dessa två metoder inom elektrokardiogramsignaler (ECG) testades. En tredje metod för anomalidetektering i ECGsignaler testades.

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Book chapters on the topic "Hellinger's distance"

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Oosterhoff, J., and W. R. van Zwet. "A Note on Contiguity and Hellinger Distance." In Selected Works of Willem van Zwet, 63–72. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1314-1_6.

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Györfi, László. "Large Deviations of Hellinger Distance on Partitions." In Mathematics and Computer Science III, 531–37. Basel: Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7915-6_51.

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Chen, Hongtian, Bin Jiang, Ningyun Lu, and Wen Chen. "PCA and Hellinger Distance-Based FDD Methods." In Data-driven Detection and Diagnosis of Faults in Traction Systems of High-speed Trains, 137–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46263-5_8.

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González-Castro, Víctor, Rocío Alaiz-Rodríguez, Laura Fernández-Robles, R. Guzmán-Martínez, and Enrique Alegre. "Estimating Class Proportions in Boar Semen Analysis Using the Hellinger Distance." In Trends in Applied Intelligent Systems, 284–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13022-9_29.

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Ficsor, Tamás, and Gábor Berend. "Interpreting Word Embeddings Using a Distribution Agnostic Approach Employing Hellinger Distance." In Text, Speech, and Dialogue, 197–205. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58323-1_21.

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Spehner, D., F. Illuminati, M. Orszag, and W. Roga. "Geometric Measures of Quantum Correlations with Bures and Hellinger Distances." In Quantum Science and Technology, 105–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53412-1_6.

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"THE USE OF HELLINGER DISTANCE IN GRAPHICAL DISPLAYS OF CONTINGENCY TABLE DATA." In Multivariate Statistics and Matrices in Statistics, 143–62. De Gruyter, 1995. http://dx.doi.org/10.1515/9783112314210-014.

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Conference papers on the topic "Hellinger's distance"

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Vidyashankar, Anand N., and Jie Xu. "Stochastic optimization using Hellinger distance." In 2015 Winter Simulation Conference (WSC). IEEE, 2015. http://dx.doi.org/10.1109/wsc.2015.7408528.

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Frasca, Marco, and Riccardo Liberati. "Riemann manifolds from Hellinger distance." In 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS 2012). IEEE, 2012. http://dx.doi.org/10.1109/tywrrs.2012.6381103.

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Torra, Vicenc, Yasuo Narukawa, Michio Sugeno, and Michael Carlson. "Hellinger distance for fuzzy measures." In The 8th conference of the European Society for Fuzzy Logic and Technology. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/eusflat.2013.82.

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Lyon, R. J., J. M. Brooke, J. D. Knowles, and B. W. Stappers. "Hellinger Distance Trees for Imbalanced Streams." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.344.

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Akash, Pritom Saha, Md Eusha Kadir, Amin Ahsan Ali, and Mohammad Shoyaib. "Inter-node Hellinger Distance based Decision Tree." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/272.

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This paper introduces a new splitting criterion called Inter-node Hellinger Distance (iHD) and a weighted version of it (iHDw) for constructing decision trees. iHD measures the distance between the parent and each of the child nodes in a split using Hellinger distance. We prove that this ensures the mutual exclusiveness between the child nodes. The weight term in iHDw is concerned with the purity of individual child node considering the class imbalance problem. The combination of the distance and weight term in iHDw thus favors a partition where child nodes are purer and mutually exclusive, and skew insensitive. We perform an experiment over twenty balanced and twenty imbalanced datasets. The results show that decision trees based on iHD win against six other state-of-the-art methods on at least 14 balanced and 10 imbalanced datasets. We also observe that adding the weight to iHD improves the performance of decision trees on imbalanced datasets. Moreover, according to the result of the Friedman test, this improvement is statistically significant compared to other methods.

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Ferrante, Augusto, Michele Pavon, and Federico Ramponi. "Constrained spectrum approximation in the Hellinger distance." In European Control Conference 2007 (ECC). IEEE, 2007. http://dx.doi.org/10.23919/ecc.2007.7068705.

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Xi, Xi, Feng-qin Zhang, and Zhe Lian. "Implicit Trust Relation Extraction Based on Hellinger Distance." In 2017 13th International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2017. http://dx.doi.org/10.1109/skg.2017.00046.

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Ditzler, Gregory, and Robi Polikar. "Hellinger distance based drift detection for nonstationary environments." In 2011 Ieee Symposium On Computational Intelligence In Dynamic And Uncertain Environments - Part Of 17273 - 2011 Ssci. IEEE, 2011. http://dx.doi.org/10.1109/cidue.2011.5948491.

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Tang, Jin, Yu Cheng, and Chi Zhou. "Sketch-Based SIP Flooding Detection Using Hellinger Distance." In GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference. IEEE, 2009. http://dx.doi.org/10.1109/glocom.2009.5426267.

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Suciu, Serban, and Aurelian Isar. "Gaussian geometric discord in terms of Hellinger distance." In TIM14 PHYSICS CONFERENCE - PHYSICS WITHOUT FRONTIERS. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4937239.

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Reports on the topic "Hellinger's distance"

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Eslinger, Paul W., and Wayne A. Woodward. Minimum Hellinger Distance Estimation for Normal Models. Fort Belvoir, VA: Defense Technical Information Center, October 1990. http://dx.doi.org/10.21236/ada228714.

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Rao, C. R. An Alternative to Correspondence Analysis Using Hellinger Distance. Fort Belvoir, VA: Defense Technical Information Center, May 1997. http://dx.doi.org/10.21236/ada325255.

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