Simplified Analysis of Incomplete Data on Risk

Contradictious results published by different authors about the dynamics of systems with random parameters have been examined. Statistical analysis of the simple 1st order system proves that the random parameter can cause a systematic difference in the dynamic behavior that cannot be (in general) described by the usual constant-parameter model with the additive noise at the output.

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Corrigendum to “Bayesian test for asymmetry and nonstationarity in MTAR model with possibly incomplete data”

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2005.07.014 ◽

2006 ◽

Vol 50 (2) ◽

pp. 584

Author(s):

Soo Jung Park ◽

Dong Wan Shin ◽

Byeong Uk Park ◽

Woo Chul Kim ◽

Man-Suk Oh

Keyword(s):

Incomplete Data ◽

Bayesian Test

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Simplified analysis and design for elevated temperature components of Monju

Nuclear Engineering and Design ◽

10.1016/0029-5493(87)90008-2 ◽

1987 ◽

Vol 98 (3) ◽

pp. 305-317 ◽

Cited By ~ 34

Author(s):

Kunihiro Iida ◽

Yasuhide Asada ◽

Kunio Okabayashi ◽

Takashi Nagata

Keyword(s):

Elevated Temperature ◽

Analysis And Design ◽

Simplified Analysis

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A new imputation method based on genetic programming and weighted KNN for symbolic regression with incomplete data

Soft Computing ◽

10.1007/s00500-021-05590-y ◽

2021 ◽

Author(s):

Baligh Al-Helali ◽

Qi Chen ◽

Bing Xue ◽

Mengjie Zhang

Keyword(s):

Genetic Programming ◽

Incomplete Data ◽

Symbolic Regression ◽

Imputation Method

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A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Mathematics ◽

10.3390/math9070786 ◽

2021 ◽

Vol 9 (7) ◽

pp. 786

Author(s):

Yenny Villuendas-Rey ◽

Eley Barroso-Cubas ◽

Oscar Camacho-Nieto ◽

Cornelio Yáñez-Márquez

Keyword(s):

Swarm Intelligence ◽

Data Clustering ◽

Incomplete Data ◽

Missing Values ◽

Clustering Algorithms ◽

Bat Algorithm ◽

Hybrid Features ◽

Bee Colony ◽

Learning Tasks ◽

Clustering Data

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

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