Comparison of Stochastic Identification Techniques for Dynamic Modelling of Plastics Extrusion Processes

1978 ◽  
Vol 192 (1) ◽  
pp. 299-309 ◽  
Author(s):  
A. K. Kochhar ◽  
J. Parnaby
Keyword(s):  
Spectral Analysis ◽  
Maximum Likelihood ◽  
Computer Control ◽  
Dynamic Modelling ◽  
Generalized Least Squares ◽  
Extrusion Process ◽  
Correlation And Spectral Analysis ◽  
Comparison Of The Results ◽  
Identification Techniques ◽  
Plastics Extrusion

The important plastics extrusion process is briefly described and the difficulties of modelling the process from physical considerations are outlined. A number of stochastic process identification techniques, i.e. correlation, spectral analysis, generalized least squares, instrumental variable, correlation matching, maximum likelihood and Box-Jenkins algorithms are briefly reviewed. The results of experimental work carried out on a laboratory plastics extruder, using random perturbations in screw speed, are presented. From a comparison of the results of different identification methods, it is suggested that although correlation and spectral analysis techniques can help in improving the understanding of the process mechanisms, the type of models best suited for high level feed-forward computer control are of the Box-Jenkins and maximum likelihood structural forms.

Estimating an Autoregressive Current Effects Model of Sales Response when Observations are Aggregated over Time: Least Squares versus Maximum Likelihood

1988 ◽  
Vol 25 (3) ◽  
pp. 301-307
Author(s):  
Wilfried R. Vanhonacker
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Serial Correlation ◽  
Mean Squared Error ◽  
Generalized Least Squares ◽  
Parameter Estimates ◽  
Squared Error ◽  
Positive Serial Correlation ◽  
Serial Correlation Coefficient ◽  
Over Time

Estimating autoregressive current effects models is not straightforward when observations are aggregated over time. The author evaluates a familiar iterative generalized least squares (IGLS) approach and contrasts it to a maximum likelihood (ML) approach. Analytic and numerical results suggest that (1) IGLS and ML provide good estimates for the response parameters in instances of positive serial correlation, (2) ML provides superior (in mean squared error) estimates for the serial correlation coefficient, and (3) IGLS might have difficulty in deriving parameter estimates in instances of negative serial correlation.

scholarly journals Comparative Analysis of Some Structural Equation Model Estimation Methods with Application to Coronary Heart Disease Risk

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
David Adedia ◽  
Atinuke O. Adebanji ◽  
Simon Kojo Appiah
Keyword(s):  
Coronary Heart Disease ◽  
Maximum Likelihood ◽  
Least Squares ◽  
Asymptotic Distribution ◽  
Structural Equation ◽  
Generalized Least Squares ◽  
Least Squares Estimator ◽  
Coronary Risk ◽  
Distribution Free ◽  
Better Than

This study compared a ridge maximum likelihood estimator to Yuan and Chan (2008) ridge maximum likelihood, maximum likelihood, unweighted least squares, generalized least squares, and asymptotic distribution-free estimators in fitting six models that show relationships in some noncommunicable diseases. Uncontrolled hypertension has been shown to be a leading cause of coronary heart disease, kidney dysfunction, and other negative health outcomes. It poses equal danger when asymptomatic and undetected. Research has also shown that it tends to coexist with diabetes mellitus (DM), with the presence of DM doubling the risk of hypertension. The study assessed the effect of obesity, type II diabetes, and hypertension on coronary risk and also the existence of converse relationship with structural equation modelling (SEM). The results showed that the two ridge estimators did better than other estimators. Nonconvergence occurred for most of the models for asymptotic distribution-free estimator and unweighted least squares estimator whilst generalized least squares estimator had one nonconvergence of results. Other estimators provided competing outputs, but unweighted least squares estimator reported unreliable parameter estimates such as large chi-square test statistic and root mean square error of approximation for Model 3. The maximum likelihood family of estimators did better than others like asymptotic distribution-free estimator in terms of overall model fit and parameter estimation. Also, the study found that increase in obesity could result in a significant increase in both hypertension and coronary risk. Diastolic blood pressure and diabetes have significant converse effects on each other. This implies those who are hypertensive can develop diabetes and vice versa.

scholarly journals Application of time-series analyses to the hydrological functioning of an Alpine karstic system: the case of Bange-L’Eau-Morte

2004 ◽  
Vol 8 (6) ◽  
pp. 1051-1064 ◽  
Author(s):  
T. Mathevet ◽  
M. l. Lepiller ◽  
A. Mangin
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Analysis ◽  
Noise Analysis ◽  
Recession Curves ◽  
Time Series Analyses ◽  
Correlation And Spectral Analysis ◽  
Wavelet Analyses ◽  
Dependent Behaviour ◽  
Hydrological Functioning

Abstract. This paper analyses the hydrological functioning of the Bange-L’Eau-Morte karstic system using classical and original techniques, recession curves, correlation and spectral analyses, noise analysis and wavelet analyses. The main characteristics that can be deduced are the recession coefficients, the dynamic volume of storage, the response time of the system, the quickflow and baseflow components and the snowmelt characteristics. The non-stationary and timescale-dependent behaviour of the system is studied and particular features of the runoff are shown. The step-by-step use of these different techniques provides a general methodology applicable to different karstic systems to provide quantifiable and objective criteria for differentiation and comparison of karstic systems. Keywords: karstic hydrology, Bauges mountains, recession curves, correlation and spectral analysis, wavelet analysis, snowmelt

Comparison of Maximum Likelihood, Generalized Least Squares, Ordinary Least Squares, and Asymptotically Distribution Free Parameter Estimates in Drug Abuse Latent Variable Causal Models

1983 ◽  
Vol 13 (4) ◽  
pp. 387-404 ◽  
Author(s):  
G. J. Huba ◽  
L. L. Harlow
Keyword(s):  
Drug Abuse ◽  
Maximum Likelihood ◽  
Least Squares ◽  
Latent Variable ◽  
Generalized Least Squares ◽  
Causal Models ◽  
Multivariate Normal ◽  
Distribution Free ◽  
Asymptotically Distribution Free ◽  
Normally Distributed

Latent variable causal modeling techniques are sometimes criticized when applied to drug abuse data because the commonly-employed maximum likelihood parameter estimation method requires that the data be normally distributed for the statistical tests to be accurate. In this article, four estimators for the parameters in two large latent variable causal models are compared in real drug abuse datasets. One estimator does not require that the data be multivariate normal and does, in fact, correct for data non-normality. Specifically, maximum likelihood and generalized least squares estimators for normally-distributed variables are compared with Browne's asymptotically distribution free techniques for continuous non-normally distributed data. Additionally, ordinary (unweighted) least squares estimates are used. Descriptions of the techniques are given and actual results in two “real” datasets are provided. It is concluded that the distribution free technique provides results which are generally comparable to those obtained with maximum likelihood estimation for datasets which depart in typical ways from the ideal of the multivariate normal distribution.

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