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Genetic algorithms for the selection of smoothing parameters in additive models

Krause, Rüdiger; Tutz, Gerhard - In: Computational Statistics 21 (2006) 1, pp. 9-31

Persistent link: https://www.econbiz.de/10005613152

Non-crossing quantile regression via doubly penalized kernel machine

Shim, Jooyong; Hwang, Changha; Seok, Kyung - In: Computational Statistics 24 (2009) 1, pp. 83-94

Persistent link: https://www.econbiz.de/10005382515

Feature clustering for instrument classification

Ligges, Uwe; Krey, Sebastian - In: Computational Statistics 26 (2011) 2, pp. 279-291

Persistent link: https://www.econbiz.de/10009149754

Computing an Element in the Lexicographic Kernel of a Game

Faigle, Ulrich; Kern, Walter; Kuipers, Jeroen - In: Computational Statistics 63 (2006) 3, pp. 427-433

The lexicographic kernel of a game lexicographically maximizes the surplusses s ij (rather than the excesses as would … the nucleolus) and is contained in both the least core and the kernel. We show that an element in the lexicographic kernel … determine a kernel element without appealing to Maschler transfers in the execution of the algorithm. Copyright Springer …

Persistent link: https://www.econbiz.de/10010847867

The prenucleolus and the prekernel for games with communication structures

Khmelnitskaya, Anna; Sudhölter, Peter - In: Computational Statistics 78 (2013) 2, pp. 285-299

It is well-known that the prekernel on the class of TU games is uniquely determined by non-emptiness, Pareto efficiency (EFF), covariance under strategic equivalence (COV), the equal treatment property, the reduced game property (RGP), and its converse. We show that the prekernel on the class of...

Persistent link: https://www.econbiz.de/10010847974

Contribution to the bandwidth choice for kernel density estimates

Horová, Ivana; Zelinka, Jiří - In: Computational Statistics 22 (2007) 1, pp. 31-47

Persistent link: https://www.econbiz.de/10005613181

A bias reducing technique in kernel distribution function estimation

Kim, Choongrak; Kim, Sungsoo; Park, Mira; Lee, Hakbae - In: Computational Statistics 21 (2006) 3, pp. 589-601

Persistent link: https://www.econbiz.de/10005613214

Functional data classification: a wavelet approach

Chang, Chung; Chen, Yakuan; Ogden, R. - In: Computational Statistics 29 (2014) 6, pp. 1497-1513

-dimensional curves or two- or three-dimensional images). One popular general approach is based on the kernel-based method, proposed by …

Persistent link: https://www.econbiz.de/10011151859

Sparse distance metric learning

Choy, Tze; Meinshausen, Nicolai - In: Computational Statistics 29 (2014) 3, pp. 515-528

Nearest neighbour classification requires a good distance metric. Previous approaches try to learn a quadratic distance metric learning so that observations of different classes are well separated. For high-dimensional problems, where many uninformative variables are present, it is attractive to...

Persistent link: https://www.econbiz.de/10010998471

Second-order least-squares estimation for regression models with autocorrelated errors

Rosadi, Dedi; Peiris, Shelton - In: Computational Statistics 29 (2014) 5, pp. 931-943

the consistency and asymptotic normality of the proposed estimator and provide a simulation study to compare its …

Persistent link: https://www.econbiz.de/10010998478