The number of partitions identified in a cluster analysis is traditionally a critical point of the procedure. There are many solutions available in the literature that researchers can exploit to guide how they determi...
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The number of partitions identified in a cluster analysis is traditionally a critical point of the procedure. There are many solutions available in the literature that researchers can exploit to guide how they determine the number of clusters. However, when a statistical analysis requires repeated cluster analyses, such as when tracking the changing composition of clusters over time, an automated approach can be beneficial. We propose a method to automatically cut dendrograms generated by a hierarchical clustering technique using a novel algorithm called Model-Based recursivepartitioning. As a case study, the method is applied to dynamically analyze the interdependencies between industry sectors during the pandemic period.
Space Information Flow (SIF) is a new research paradigm that studies network coding in a geometric space, which is different with Network Information Flow (NIF) that studies network coding in a graph. One of the key o...
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ISBN:
(纸本)9781479935130
Space Information Flow (SIF) is a new research paradigm that studies network coding in a geometric space, which is different with Network Information Flow (NIF) that studies network coding in a graph. One of the key open problems at the core of SIF is to design an algorithm that computes optimal SIF solutions. A new heuristic SIF algorithm based on non-uniform recursive space partitioning is proposed in this work, for computing SIF for any density distribution of given terminal nodes in 2-D Euclidean space. Simulation results show that the new algorithm has low computational complexity and converges to optimal solutions promptly.
The Lanczos algorithm with a new recursivepartitioning method to compute the eigenvalues, in a given specified interval, is presented in this paper. Comparisons have been made respecting the numerical results as well...
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The Lanczos algorithm with a new recursivepartitioning method to compute the eigenvalues, in a given specified interval, is presented in this paper. Comparisons have been made respecting the numerical results as well as the CPU-time with that of the Sturm sequence-bisection method. (C) 1999 Elsevier Science Ltd. All rights reserved.
In this paper, the computation of the smallest eigenvalues and the corresponding eigenvectors of the generalized eigenvalue problem using Lanczos algorithm with a recursivepartitioning method as well as the Sturm seq...
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In this paper, the computation of the smallest eigenvalues and the corresponding eigenvectors of the generalized eigenvalue problem using Lanczos algorithm with a recursivepartitioning method as well as the Sturm sequence-bisection method have been discussed. We have also presented the comparison of the numerical results and the CPU-time between the above two methodologies. Our comparative study indicates that the Lanczos with a recursivepartitioning method takes relatively less computing time than that of the Sturm sequence-bisection method. (C) 2000 Elsevier Science Ltd. All rights reserved.
In some cancer clinical studies, researchers have interests to explore the risk factors associated with competing risk outcomes such as recurrence-free survival. We develop a novel recursivepartitioning framework on ...
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In some cancer clinical studies, researchers have interests to explore the risk factors associated with competing risk outcomes such as recurrence-free survival. We develop a novel recursivepartitioning framework on competing risk data for both prognostic and predictive model constructions. We define specific splitting rules, pruning algorithm, and final tree selection algorithm for the competing risk tree models. This methodology is quite flexible that it can corporate both semiparametric method using Cox proportional hazards model and parametric competing risk model. Both prognostic and predictive tree models are developed to adjust for potential confounding factors. Extensive simulations show that our methods have well-controlled type I error and robust power performance. Finally, we apply both Cox proportional hazards model and flexible parametric model for prognostic tree development on a retrospective clinical study on oropharyngeal cancer patients.
In the current world with more enhancing techniques like recursivepartitioning approach is used for the identification of Diabetes. Hyperglycemia is a symptom of diabetes mellitus, which is a chronic disorder. It is ...
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ISBN:
(纸本)9781665426428
In the current world with more enhancing techniques like recursivepartitioning approach is used for the identification of Diabetes. Hyperglycemia is a symptom of diabetes mellitus, which is a chronic disorder. It is due to metabolic disorders and the reduction of blood glucose levels in the body. There are stages in diabetes based on the severity. So, it is an emergency task to detect diabetes early to reduce the severity of the problem. By considering all the complications, many research studies had gone through to solve the problem efficiently and effectively. Using a recursivepartitioning approach like Random forest, the accuracy of identifying diabetes has been improved. This approach provides a substantial improvement of performance over prevailing practices. recursivepartitioning may be a non-parametric modeling technique that is widely utilized in classification and regression problems. It is a standard method used for decision trees. recursivepartitioning is a top-down greedy algorithm that make optimized choices locally at each step.
Context. Physicians overestimate survival in patients with advanced cancer. Patient-reported outcomes could provide another way to estimate survival. We previously reported four prognostic groups based on Karnofsky Pe...
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Context. Physicians overestimate survival in patients with advanced cancer. Patient-reported outcomes could provide another way to estimate survival. We previously reported four prognostic groups based on Karnofsky Performance Status, Functional Assessment of Cancer Therapy physical well-being subscale, and Memorial Symptom Assessment Scale-Short Form physical symptom distress subscale scores. Objectives. To determine the validity of these four prognostic groups. Methods. We performed prospective surveys. Data from a total of 880 Veterans Affairs Medical Center patients, 417 in the First Cohort and 463 in the Validation Cohort, were analyzed. Both inpatients and outpatients were prospectively recruited in Institutional Review Board-approved studies from August 1999 to September 2009. Survival was measured from the date of entry until death or December 1, 2009. Patients completed self-assessments with the Functional Assessment of Cancer Therapy and Memorial Symptom Assessment Scale-Short Form. Analysis of variance was used to test differences between groups in continuous variables;a generalized Wilcoxon test was used for differences between groups for survival. Results. The average age in the Validation Cohort was 66.5 years and 98% were men. The majority of patients had metastatic cancer (90%), with lung (28%) and prostate (26%) cancers being predominant. The median Karnofsky Performance Status was 70. Median survival was 33, 46.5, 124, and 209.5 days for the four prognostic groups (P < 0.0001, all pair-wise comparisons P < 0.02). Conclusion. The four prognostic groups remained distinct in the prospective cohort. Small differences in patient-reported physical well-being can halve survival estimates. Patient-reported outcomes can correct for physician overestimate of prognosis. This study provides a way to use patient-reported outcomes for prognosis in patients with advanced cancer, with important implications for assessment. Published by Elsevier Inc. on behalf of A
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