Displaying similar documents to “Estimating Parameters of a Multiplicative Seasonal ARIMA Model Using Prediction Error Method Algorithm”

Em Algorithm for MLE of a Probit Model for Multiple Ordinal Outcomes

Grigorova, Denitsa, Encheva, Elitsa, Gueorguieva, Ralitza (2013)

Serdica Journal of Computing

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The correlated probit model is frequently used for multiple ordered data since it allows to incorporate seamlessly different correlation structures. The estimation of the probit model parameters based on direct maximization of the limited information maximum likelihood is a numerically intensive procedure. We propose an extension of the EM algorithm for obtaining maximum likelihood estimates for a correlated probit model for multiple ordinal outcomes. The algorithm is implemented in...

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

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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...

On the properties typical of economic time series.

Arthur B. Treadway (1984)

Qüestiió

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This paper summarizes the results of econometric time-series analysis performed by the author and colleagues over the last seven years, using the Box-Jenkins approach in interaction with Economic Theory. Typical univariate properties, typical data anomalies and typical relationships are described. Common practice in Econometrics is criticized and certain aspects of Economic Theory are discussed.

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

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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...

Problems in scientific time series analysis.

Granville Tunnicliffe Wilson (1984)

Qüestiió

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The paper reviews the statistical methods of time series analysis used in a selection of papers from respected scientific journals. In particular, problems are considered in the search for cycles, the use of regression to establish causal links between variables, transfer function modelling and the use of filtering to extract componentes of time series. An attempt is made to assess how useful the ideas of ARMA and Transfer Function modelling might be in improving the efficiency...

Identifiability and estimation of pharmacokinetic parameters for the ligands of the macrophage mannose receptor

Nathalie Verdiere, Lilianne Denis-Vidal, Ghislaine Joly-Blanchard, Dominique Domurado (2005)

International Journal of Applied Mathematics and Computer Science

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The aim of this paper is numerical estimation of pharmacokinetic parameters of the ligands of the macrophage mannose receptor, without knowing it a priori the values of these parameters. However, it first requires a model identifiability analysis, which is done by applying an algorithm implemented in a symbolic computation language. It is shown that this step can lead to a direct numerical estimation algorithm. In this way, a first estimate is computed from noisy simulated observations...

On naive Bayes in speech recognition

László Tóth, András Kocsor, János Csirik (2005)

International Journal of Applied Mathematics and Computer Science

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The currently dominant speech recognition technology, hidden Mar-kov modeling, has long been criticized for its simplistic assumptions about speech, and especially for the naive Bayes combination rule inherent in it. Many sophisticated alternative models have been suggested over the last decade. These, however, have demonstrated only modest improvements and brought no paradigm shift in technology. The goal of this paper is to examine why HMM performs so well in spite of its incorrect...