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Data driven estimates for mixtures

Beatriz Vaz de Melo Mendes, Hedibert Freitas Lopes
2004 Computational Statistics & Data Analysis  
This procedure provides an analytical expression for the mixture distribution of the data, which may be used in simulations and construction of scenarios, while providing an accurate estimation of quantiles  ...  The performance of the proposed data driven procedure is assessed by simulation experiments and also by its application to real data.  ...  Data driven procedure We assume that the data generating process may be a mixture of di erent structures, or made up from percentages of di erent distributions.  ... 
doi:10.1016/j.csda.2003.12.006 fatcat:3dytk22fjvfvlhdvj2rnyf567a

QuickSel: Quick Selectivity Learning with Mixture Models [article]

Yongjoo Park, Shucheng Zhong, Barzan Mozafari
2018 arXiv   pre-print
Unlike query-driven histograms, QuickSel relies on a mixture model and a new optimization algorithm for training its model.  ...  Instead, it builds an internal model of the underlying data, which can be refined significantly faster (e.g., only 1.9 milliseconds for 300 queries).  ...  Our Model To overcome the limitations of query-driven histograms, we use a mixture model [19] to capture the unknown distribution of the data.  ... 
arXiv:1812.10568v1 fatcat:4rzhgjby3rcxrbollvy6zwjrue

Optical performance monitoring via histogram: A data-driven approach

Yonggang Wen, Kevin W. Wilson
2009 2009 14th OptoElectronics and Communications Conference  
We apply three alternative statistical learning methods to estimate optical transmission impairments (e.g., noises, chromatic dispersion) from synchronous histograms.  ...  data and then applying statistical learning algorithms.  ...  Our numerical investigation indicates that the estimation is quite accurate, especially when using locally weighted regression.  ... 
doi:10.1109/oecc.2009.5222713 fatcat:qntogy5ukzb3topi7gu6pv4cvm

Vapor-liquid equilibrium predictions of n-alkane/nitrogen mixtures using neural networks [article]

Suman Chakraborty, Yixuan Sun, Guang Lin, Li Qiao
2020 arXiv   pre-print
Two data-driven models have been proposed in this study, each of which was competent in estimating VLE for the binary systems of C10/N2 and C12/N2, at pressures ranging up to 50-60 MPa.  ...  The main objective of this study was to develop data-driven models to predict VLE of Type III binary mixtures involving long-chained n-alkanes and nitrogen.  ...  Model performance in estimating mixture equilibrium pressure: data driven interpolation model (DD-Int) vs PR-EOS for the binary mixtures of (a) C10/N2 and (b) C12/N2 5.3 Unified Data-Driven Model using  ... 
arXiv:2012.12928v1 fatcat:o35eibx4frgqlpukbh6mk2hobq

Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation

Sarah Depaoli
2013 Psychological methods  
informative priors, data-driven informative priors, priors reflecting partial-knowledge of parameters, and "inaccurate" (but informative) priors.  ...  Growth mixture modeling (GMM) represents a technique that is designed to capture change over time for unobserved subgroups (or latent classes) that exhibit qualitatively different patterns of growth.  ...  using weak priors; Data-driven ϭ Bayesian estimation using data-driven priors; MD ϭ Mahalanobis distance.  ... 
doi:10.1037/a0031609 pmid:23527607 fatcat:2kwg4hdifrgbjkuibx6atxpkwu

State-Space Models for Binomial Time Series with Excess Zeros [chapter]

Fan Tang, Joseph E. Cavanaugh
2018 Time Series Analysis and Applications  
However, for data arising from a binomial mixture distribution, a survey of the literature for analogous frameworks reflects an absence of work dealing with binomial time series with excess zeros.  ...  To handle such data, we propose two general classes of models: a class of observation-driven ZIB (ODZIB) models, and a class of parameter-driven ZIB (PDZIB) models.  ...  To develop an EM algorithm for parameter estimation of the mixture model, Eqs. (15)- (17) , we begin by formulating the complete-data likelihood; i.e., the joint density of s 0 : n , u 1 : n , and y 1  ... 
doi:10.5772/intechopen.71336 fatcat:oyndhei5obh3dlfh65m4lhn3ke

Alternating kernel and mixture density estimates

Carey E. Priebe, David J. Marchette
2000 Computational Statistics & Data Analysis  
We describe and investigate a data-driven procedure for obtaining parsimonious mixture model estimates or, conversely, kernel estimates with data-driven local smoothing properties.  ...  The main idea is to obtain a semiparametric estimate by alternating between the parametric and nonparametric viewpoints.  ...  ÿnite mixture model estimate with data-driven complexity that we seek.  ... 
doi:10.1016/s0167-9473(00)00003-7 fatcat:nnfubtp6t5aqjd5dmpoop6o2mq

Causal Associations of Urate With Cardiovascular Risk Factors: Two-Sample Mendelian Randomization

Thitiya Lukkunaprasit, Sasivimol Rattanasiri, Boonsong Ongphiphadhanakul, Gareth J. McKay, John Attia, Ammarin Thakkinstian
2021 Frontiers in Genetics  
SNPs were selected by statistically and biologically driven approaches as instrumental variables.  ...  nucleotide polymorphisms (SNPs) as instrumental variables using two-sample MR and various sensitivity analyses.MethodsData on SNP-urate associations were taken from the Global Urate Genetics Consortium and data  ...  providing the summary data used in this study.  ... 
doi:10.3389/fgene.2021.687279 pmid:34306027 pmcid:PMC8297413 fatcat:6eytncbvnbglpdf2enlhpstbqi

Data Driven Robust Energy and Reserve Dispatch Based on a Nonparametric Dirichlet Process Gaussian Mixture Model

Li Dai, Dahai You, Xianggen Yin
2020 Energies  
To avoid such conservative results from traditional robust optimization methods, in this paper a novel data driven optimization method based on the nonparametric Dirichlet process Gaussian mixture model  ...  Finally, a column and constraint generation method was employed to solve the proposed data driven optimization method.  ...  Based on these estimated DPGMM parameters, we developed a data driven polyhedral uncertainty set for the wind power forecast error.  ... 
doi:10.3390/en13184642 fatcat:rrktgptozfaazcrzrhzgb74c5a

Data-Driven And Physical Model-Based Designs Of Probabilistic Spatial Dictionary For Online Meeting Diarization And Adaptive Beamforming

Shoko Araki, Nobutaka Ito, Tomohiro Nakatani
2018 Zenodo  
Mask Estimation In mask estimation, masks M (n) tf , n = 0, 1, . . . , N, are estimated by using the mixture weights and the dictionary (3) .  ...  The data-driven design gave a lower WER for the speech dictionary, while the physical model-based design for the noise dictionary.  ... 
doi:10.5281/zenodo.1159668 fatcat:hjl5sipn5rd2pj6gapfadnrluu

Model-Based Filtering via Finite Skew Normal Mixture for Stock Data

Solmaz Yaghoubi, Rahman Farnoosh
2020 Journal of Statistical Theory and Applications (JSTA)  
The proposed model estimates matrices of time-varying parameter for skew normal distribution mixture using EM algorithm, updating the estimated parameters via generalized autoregressive score (GAS) model  ...  Empirical studies are conducted to examine the effect of the proposed model in clustering, estimating, and updating parameters for real data from 12 sets of stocks.  ...  There is also no "budget statement" for this article. We also appreciate from Referee and associate editor who led to a number of improvements.  ... 
doi:10.2991/jsta.d.200827.001 fatcat:5vfi5pxoufgjdks2mmdxxtgqgm

Adaptive mixture density estimation

Carey E. Priebe, David J. Marchette
1993 Pattern Recognition  
The asymptotic performance of the method, dubbed "adaptive mixtures" (Priebe and Marchette, Pattern Recognition 24, 1197-1209 (1991)) for its data-driven development of a mixture model approximation to  ...  A~tract--A recursive, nonparametric method is developed for performing density estimation derived from mixture models, kernel estimation and stochastic approximation.  ...  the complexity of the model data-driven, while the recur-sive maximum likelihood estimation of the individual variances, inherent in U(.), yields the data-driven smoothing described at the outset.  ... 
doi:10.1016/0031-3203(93)90130-o fatcat:abksxld2rrhxnfwzm534tu3hvy

Remaining Useful Life Estimation of Critical Components With Application to Bearings

Kamal Medjaher, Diego Alejandro Tobon-Mejia, Noureddine Zerhouni
2012 IEEE Transactions on Reliability  
RUL estimation can be done by using two main approaches, namely model-based and data-driven approaches.  ...  For this purpose, Mixture of Gaussians Hidden Markov Models (MoG-HMMs), represented by Dynamic Bayesian Networks (DBNs), are used as a modeling tool.  ...  This paper deals with a data-driven prognostics method for the estimation of the RUL of critical physical components.  ... 
doi:10.1109/tr.2012.2194175 fatcat:yxl6hl3chvglza3l6yefeyhv5e

Independent component analysis: an introduction

James V. Stone
2002 Trends in Cognitive Sciences  
Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set.  ...  McKeown for comments on this paper.  ...  Model-based vs data-driven methods An ongoing debate in the analysis of biomedical data concerns the relative merits of model-based versus data-driven methods [9] .  ... 
doi:10.1016/s1364-6613(00)01813-1 pmid:15866182 fatcat:z77j2rt33fbnrniwbxmgqmnvfi

Mistake-driven mixture of hierarchical tag context trees

Masahiko Haruno, Yuji Matsumoto
1997 Proceedings of the 35th annual meeting on Association for Computational Linguistics -  
This paper proposes a mistake-driven mixture method for learning a tag model.  ...  The method iteratively performs two procedures: 1. constructing a tag model based on the current data distribution and 2. updating the distribution by focusing on data that are not well predicted by the  ...  Our experinaental results show that combining hierarchical tag context trees with the mistake-driven mixture method is extremely effective for 1. incorporating exceptional connections and 2. avoiding data  ... 
doi:10.3115/976909.979647 dblp:conf/acl/HarunoM97 fatcat:l4zq2pfjrzdyben6uvltzxz4ru
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