Displaying similar documents to “Alternative models in precipitation analysis.”

A rainfall forecasting method using machine learning models and its application to the Fukuoka city case

S. Monira Sumi, M. Faisal Zaman, Hideo Hirose (2012)

International Journal of Applied Mathematics and Computer Science

Similarity:

In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid...

Statistical procedures for spatial point pattern recognition.

Jorge Mateu (2002)

Qüestiió

Similarity:

Spatial structures in the form of point patterns arise in many different contexts, and in most of them the key goal concerns the detection and recognition of the underlying spatial pattern. Particularly interesting is the case of pattern analysis with replicated data in two or more experimental groups. This paper compares design-based and model-based approaches to the analysis of this kind of spatial data. Basic questions about pattern detection concern estimating the properties of the...

Pattern-mixture models

Geert Molenberghs, Herbert Thijs, Bart Michiels, Geert Verbeke, Michael G. Kenward (2004)

Journal de la société française de statistique

Similarity:

Rough modeling - a bottom-up approach to model construction

Terje Loken, Jan Komorowski (2001)

International Journal of Applied Mathematics and Computer Science

Similarity:

Traditional data mining methods based on rough set theory focus on extracting models which are good at classifying unseen obj-ects. If one wants to uncover new knowledge from the data, the model must have a high descriptive quality-it must describe the data set in a clear and concise manner, without sacrificing classification performance. Rough modeling, introduced by Kowalczyk (1998), is an approach which aims at providing models with good predictive emphand descriptive qualities, in...

Advances in parallel heterogeneous genetic algorithms for continuous optimization

Enrique Alba, Francisco Luna, Antonio Nebro (2004)

International Journal of Applied Mathematics and Computer Science

Similarity:

In this paper we address an extension of a very efficient genetic algorithm (GA) known as Hy3, a physical parallelization of the gradual distributed real-coded GA (GD-RCGA). This search model relies on a set of eight subpopulations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results in continuous optimization by using crossover operators tuned to explore and exploit the solutions...

Application of the Rasch model in categorical pedigree analysis using MCEM: I binary data

G. Qian, R. M. Huggins, D. Z. Loesch (2004)

Discussiones Mathematicae Probability and Statistics

Similarity:

An extension of the Rasch model with correlated latent variables is proposed to model correlated binary data within families. The latent variables have the classical correlation structure of Fisher (1918) and the model parameters thus have genetic interpretations. The proposed model is fitted to data using a hybrid of the Metropolis-Hastings algorithm and the MCEM modification of the EM-algorithm and is illustrated using genotype-phenotype data on a psychological subtest in families...