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Topology Adaptive Graph Estimation in High Dimensions

Johannes Lederer, Christian L. Müller
2022 Mathematics  
These simulations showed that a neighborhood selection scheme based on Lasso and an optimal (in practice unknown) tuning parameter outperformed other standard methods over a large spectrum of scenarios  ...  We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional Gaussian graphical models.  ...  Acknowledgments: We thank the Reviewers for their insightful feedback. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/math10081244 fatcat:2k743r2imbfcrppgubnjcbniyy

Topology Adaptive Graph Estimation in High Dimensions [article]

Johannes Lederer, Christian Müller
2014 arXiv   pre-print
These simulations show that a neighborhood selection scheme based on Lasso and an optimal (in practice unknown) tuning parameter outperforms other standard methods over a large spectrum of scenarios.  ...  We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional Gaussian graphical models.  ...  To approach some of the practical challenges, we introduce Graphical TREX (GTREX), a novel method for graph estimation based on neighborhood selection with TREX [Lederer and Müller, 2014] .  ... 
arXiv:1410.7279v1 fatcat:cgpiigmpsvegvlj4hnw334pfvy

Node and edge selectivity estimation for range queries in spatial networks

E. Tiakas, A.N. Papadopoulos, A. Nanopoulos, Y. Manolopoulos
2009 Information Systems  
In this paper, we present novel methods to estimate the number of nodes and edges in regions of interest in spatial networks, towards predicting the space and time requirements for range queries.  ...  The methods are evaluated by using real-life and synthetic data sets.  ...  In [1] the authors propose a selectivity estimation method with low relative estimation error (about 10%) for spatial queries using specific global parameters formulae based on Hausdorff fractal dimension  ... 
doi:10.1016/ fatcat:gii32a7q3zahbjiq7apbj67mae

Linear model selection and regularization for Serum Prostate-Specific Antigen prediction of Patients with prostate cancer using R

Gongli Li, Han Li
2021 IEEE Access  
The number of predictors in subsets is a tuning parameter. The best selected model for these three selection methods by BIC, Cp and adjusted 2 are the same.  ...  Dimension reduction method result To find the optimal tuning parameter M (number of components), ordinary CV estimate, and adjusted CV (biascorrected CV estimate) are applied to find the minimum MSE.  ...  Appendix (Algorithms for subset selection) A. Exhaustive (a) Consider all models that contain all but one of the predictors in , for a total of − 1 predictors.  ... 
doi:10.1109/access.2021.3095914 fatcat:66is7gzstbbx5ff4hqt7a7caxa

Machine Learning Approach for Bottom 40 Percent Households (B40) Poverty Classification

Nor Samsiah Sani, Mariah Abdul Rahman, Azuraliza Abu Bakar, Shahnurbanon Sahran, Hafiz Mohd Sarim
2018 International Journal on Advanced Science, Engineering and Information Technology  
Each classifier is then optimized using different tuning parameter with 10-Fold Cross Validation for achieving the optimal values before the performance of the three classifiers are compared to each other  ...  sampling methods using SMOTE has been conducted to the raw dataset to ensure the quality of the training data.  ...  , Implementation Coordination Unit of Prime Minister's Department (ICU JPM) for the data set used in the experiment.  ... 
doi:10.18517/ijaseit.8.4-2.6829 fatcat:s7jtwsfmijcttjb4n3sqkgjsry

Density Power Downweighting and Robust Inference: Some New Strategies

Saptarshi Roy, Kaustav Chakraborty, Somnath Bhadra, Ayanendranath Basu
2019 Journal of Mathematics and Statistics  
Some minimum Bregman divergence estimators and related tests of hypothesis seem to be able to do well in this respect, with the procedures based on the density power divergence providing the existing standard  ...  Preserving the robustness of the procedure has, at the present time, become almost a default requirement for statistical data analysis.  ...  A more modern class of minimum distance estimators is based on the family of Bregman divergences.  ... 
doi:10.3844/jmssp.2019.333.353 fatcat:omcc54ffynbdbjgff36n6uvdfq

Automated Tuning of an Extended Kalman Filter Using the Downhill Simplex Algorithm

Thomas D. Powell
2002 Journal of Guidance Control and Dynamics  
The filter tuning problem for a system processing simulated data is then formulated as a numerical optimization problem by defining a performance index based on state estimate errors.  ...  Because the true tuning parameters for this problem are unknown, the neighborhood of the minimum cost in the g);,q22 parameter space is located using a coarse grid search for the purpose of plotting.  ... 
doi:10.2514/2.4983 fatcat:ojptmslds5atjc2pkziqxzdape

Methods to impute missing genotypes for population data

Zhaoxia Yu, Daniel J. Schaid
2007 Human Genetics  
We evaluate the accuracy of them using single nucleotide polymorphism (SNP) data from the HapMap project, under a variety of conditions and parameters.  ...  k-nearest neighbor, KNN, and a weighted k-nearest neighbor, wtKNN), three linear regression methods (backward variable selection, LM.back, least angle regression, LM.lars, and singular value decomposition  ...  Acknowledgments The authors are grateful to the three anonymous reviewers for their constructive suggestions. This work was supported by the U.S.  ... 
doi:10.1007/s00439-007-0427-y pmid:17851696 fatcat:naw3ks4zvfgxnk2yt2xt34v7pi

Penalized Maximum Tangent Likelihood Estimation and Robust Variable Selection [article]

Yichen Qin, Shaobo Li, Yang Li, Yan Yu
2017 arXiv   pre-print
mixture of minimum Kullback-Leibler distance estimation and minimum ℓ_2 distance estimation.  ...  We introduce a new class of mean regression estimators -- penalized maximum tangent likelihood estimation -- for high-dimensional regression estimation and variable selection.  ...  We also propose a method for adaptively selecting the tuning parameter t. In addition, we establish the bound of 2 norm of the estimation error under high-dimensional settings.  ... 
arXiv:1708.05439v2 fatcat:okcikalbtfd6vfv2reub3ggmxy

Robust estimation in the normal mixture model

Hironori Fujisawa, Shinto Eguchi
2006 Journal of Statistical Planning and Inference  
A relationship between robustness and efficiency is investigated and an adaptive method for selecting the tuning parameter of the modified likelihood is suggested, based on the robust model selection criterion  ...  A modified likelihood approach suggested in Basu et al. (1998) can overcome these drawbacks.  ...  This work was supported by Grant-in-Aid for Scientific Research of the Ministry of Education, Culture, Sports, Science and Technology.  ... 
doi:10.1016/j.jspi.2005.03.008 fatcat:zhnluyrn7vafzdoxokkasfpobm

Regularized Maximum Diversification Investment Strategy †

N'Golo Koné
2020 Econometrics  
These regularization schemes involve a tuning parameter that needs to be chosen. In light of this fact, we propose a data-driven method for selecting the tuning parameter.  ...  , and the naive 1/N strategy in terms of in-sample and out-of-sample Sharpe ratio performance, and it is shown that our method yields significant Sharpe ratio improvements.  ...  We will also propose a data-driven method to select the tuning parameter.  ... 
doi:10.3390/econometrics9010001 fatcat:5yttobxabfcr3aldarfzmtpyrm

Path Loss Model Optimization Using Stochastic Hybrid Genetic Algorithm

A. Bhuvaneshwari, R. Hemalatha, T. SatyaSavithri
2018 International Journal of Engineering & Technology  
In this paper, a hybrid tuning approach is proposed by merging the stochastic Weighted Least Square method and Genetic algorithm.  ...  The least values of Mean Square error (0.2702), RMSE (0.4798) and percentage Relative error (3.96) justify the tuning precision of the hybrid method.  ...  on tuning; = parameters of the model based on tuning. n = number of experiment data set.  ... 
doi:10.14419/ijet.v7i4.10.21041 fatcat:6sqoql4hhrcmfayirl3cgy7s4i

Dynamic phase-based tuning for embedded systems using phase distance mapping

Tosiron Adegbija, Ann Gordon-Ross, Arslan Munir
2012 2012 IEEE 30th International Conference on Computer Design (ICCD)  
Since the design space for tunable systems can be very large, one of the major challenges in phase-based tuning is determining the best configuration for each phase without incurring significant tuning  ...  Phase-based tuning specializes a system's tunable parameters to the varying runtime requirements of an application's different phases of execution to meet optimization goals.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  ... 
doi:10.1109/iccd.2012.6378653 dblp:conf/iccd/AdegbijaGM12 fatcat:bee7wcm2krewnnrthptrl7yyp4

A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning [article]

Grigorios G. Anagnostopoulos, Alexandros Kalousis
2019 arXiv   pre-print
Initially, the tuning of hyperparameter k for a variety of distance metrics, and the selection of efficient data transformation schemes, proposed by relevant works, is presented.  ...  In addition, accuracy improvements are achieved in this study, by a detailed examination of the appropriate adjustment of the parameters of the data transformation schemes tested, and of the handling of  ...  ACKNOWLEDGEMENTS This work was funded by the Commission for Technology and Innovation CTI, of the Swiss federal government, in the frame of the OrbiLoc project (CTI 27908.1 PFES-ES).  ... 
arXiv:1908.06851v1 fatcat:g7umte7bwbfupk24xp2b7gziai

SCRAP: A Statistical Approach for Creating a Database Query Workload Based on Performance Bottlenecks

James Skarie, Biplob K. Debnath, David J. Lilja, Mohamed F. Mokbel
2007 2007 IEEE 10th International Symposium on Workload Characterization  
In this paper, we propose a statistical approach for creating a database query workload based on performance bottlenecks (SCRAP).  ...  Our methodology takes a query workload and a set of system configuration parameters as inputs, and selects a subset of the queries from the workload based on the similarity of performance bottlenecks.  ...  Clustering Queries In this phase, the tuning sensitive queries are clustered based on the similarities among the estimated effects of the parameters. This is done in three steps.  ... 
doi:10.1109/iiswc.2007.4362194 dblp:conf/iiswc/SkarieDLM07 fatcat:bymo3as6ura37pdibdtshuhyiy
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