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Shrinkage Parameter (shrinkage + parameter)
Selected AbstractsBayesian classification of tumours by using gene expression dataJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2005Bani K. Mallick Summary., Precise classification of tumours is critical for the diagnosis and treatment of cancer. Diagnostic pathology has traditionally relied on macroscopic and microscopic histology and tumour morphology as the basis for the classification of tumours. Current classification frameworks, however, cannot discriminate between tumours with similar histopathologic features, which vary in clinical course and in response to treatment. In recent years, there has been a move towards the use of complementary deoxyribonucleic acid microarrays for the classi-fication of tumours. These high throughput assays provide relative messenger ribonucleic acid expression measurements simultaneously for thousands of genes. A key statistical task is to perform classification via different expression patterns. Gene expression profiles may offer more information than classical morphology and may provide an alternative to classical tumour diagnosis schemes. The paper considers several Bayesian classification methods based on reproducing kernel Hilbert spaces for the analysis of microarray data. We consider the logistic likelihood as well as likelihoods related to support vector machine models. It is shown through simulation and examples that support vector machine models with multiple shrinkage parameters produce fewer misclassification errors than several existing classical methods as well as Bayesian methods based on the logistic likelihood or those involving only one shrinkage parameter. [source] An efficient computational approach for prior sensitivity analysis and cross-validationTHE CANADIAN JOURNAL OF STATISTICS, Issue 1 2010Luke Bornn Abstract Prior sensitivity analysis and cross-validation are important tools in Bayesian statistics. However, due to the computational expense of implementing existing methods, these techniques are rarely used. In this paper, the authors show how it is possible to use sequential Monte Carlo methods to create an efficient and automated algorithm to perform these tasks. They apply the algorithm to the computation of regularization path plots and to assess the sensitivity of the tuning parameter in g -prior model selection. They then demonstrate the algorithm in a cross-validation context and use it to select the shrinkage parameter in Bayesian regression. The Canadian Journal of Statistics 38:47,64; 2010 © 2010 Statistical Society of Canada La sensibilité à la loi a priori et la validation croisée sont des outils importants des statistiques bayésiennes. Toutefois, ces techniques sont rarement utilisées en pratique car les méthodes disponibles pour les implémenter sont numériquement très coûteuses. Dans ce papier, les auteurs montrent comment il est possible d'utiliser les méthodes de Monte Carlo séquentielles pour obtenir un algorithme efficace et automatique pour implémenter ces techniques. Ils appliquent cet algorithme au calcul des chemins de régularisation pour un problème de régression et à la sensibilité du paramètre de la loi a priori de Zellner pour un problème de sélection de variables. Ils appliquent ensuite cet algorithme pour la validation croisée et l'utilisent afin de sélectionner le paramètre de régularisation dans un problème de régression bayésienne. La revue canadienne de statistique 38: 47,64; 2010 © 2010 Société statistique du Canada [source] Changes in shrinkage of restored soil caused by compaction beneath heavy agricultural machineryEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 4 2008B. Schäffer Summary Compaction is a major cause of soil degradation. It affects not only the porosity of the soil, but also the soil's hydrostructural stability. Soil that is restored after temporary removal is particularly sensitive to compaction. We investigated the effects of trafficking with a heavy combine harvester on the shrinkage behaviour of a restored soil that had been gently cultivated for several years. We tested the hypothesis that compaction decreases the hydrostructural stability of restored soil by analysing simultaneously measured shrinkage and water retention curves of undisturbed soil samples. Shrinkage strongly depended on clay and organic carbon content. Taking account of this influence and normalizing the shrinkage parameters with respect to these soil properties, we found pronounced effects of trafficking on shrinkage. Ten passes with the combine harvester decreased the structural porosity by about 40% at maximum swelling and by about 30% at the shrinkage limit and increased the bulk density by 8% at maximum swelling and by 10% at the shrinkage limit, but did not significantly affect the porosity of the soil plasma. Moreover, trafficking modified shrinkage, increasing the slopes of the shrinkage curve in the basic and structural shrinkage domains by about 30% and more than 150% after 10 passes, respectively. Evidently the aggregate structure was strongly destabilized. The results indicate that the hydrostructural stability of the soil was still very sensitive to compaction by trafficking even 5 years after restoration. The analysis of shrinkage seemed well suited for the assessment of compaction effects on soil structure. [source] Bayesian classification of tumours by using gene expression dataJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2005Bani K. Mallick Summary., Precise classification of tumours is critical for the diagnosis and treatment of cancer. Diagnostic pathology has traditionally relied on macroscopic and microscopic histology and tumour morphology as the basis for the classification of tumours. Current classification frameworks, however, cannot discriminate between tumours with similar histopathologic features, which vary in clinical course and in response to treatment. In recent years, there has been a move towards the use of complementary deoxyribonucleic acid microarrays for the classi-fication of tumours. These high throughput assays provide relative messenger ribonucleic acid expression measurements simultaneously for thousands of genes. A key statistical task is to perform classification via different expression patterns. Gene expression profiles may offer more information than classical morphology and may provide an alternative to classical tumour diagnosis schemes. The paper considers several Bayesian classification methods based on reproducing kernel Hilbert spaces for the analysis of microarray data. We consider the logistic likelihood as well as likelihoods related to support vector machine models. It is shown through simulation and examples that support vector machine models with multiple shrinkage parameters produce fewer misclassification errors than several existing classical methods as well as Bayesian methods based on the logistic likelihood or those involving only one shrinkage parameter. [source] | ||||||||||