Use of the maximum likelihood technique, for fitting counting distributions

1968 ◽  
Vol 65 (3) ◽  
pp. 301-306 ◽  
Author(s):  
P.H.R. Orth ◽  
W.R. Falk ◽  
G. Jones
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Technique

Statistically optimal event detection using small array data

1990 ◽  
Vol 80 (6B) ◽  
pp. 1934-1950 ◽  
Author(s):  
A. F. Kushnir ◽  
V. M. Lapshin ◽  
V. I. Pinsky ◽  
J. Fyen
Keyword(s):  
Maximum Likelihood ◽  
Power Spectrum ◽  
Event Detection ◽  
Underground Explosion ◽  
Seismic Signals ◽  
Array Data ◽  
On Line ◽  
Small Array ◽  
Maximum Likelihood Technique ◽  
Channel Filter

Abstract A generalization of Capon's maximum-likelihood technique for detection and estimation of seismic signals is introduced. By using a multi-dimensional autoregressive approximation of seismic array noise, we have developed a technique to use Capon's multi-channel filter for on-line processing. Such autoregressive adaptation to the curent noise matrix power spectrum is shown to yield good suppression of mutually correlated array noise processes. As an example, this technique is applied to detection of a small Semipalatinsk underground explosion recorded at the ARCESS array.

SAMPLING SYSTEMS FOR THE BIRCH CASEBEARER (LEPIDOPTERA: COLEOPHORIDAE)

1979 ◽  
Vol 111 (8) ◽  
pp. 875-882
Author(s):  
A. G. Raske ◽  
M. Alvo
Keyword(s):  
Maximum Likelihood ◽  
Sampling Plan ◽  
New Method ◽  
Betula Papyrifera ◽  
White Birch ◽  
Destructive Sampling ◽  
Damage Level ◽  
Sampling Systems ◽  
Non Destructive ◽  
Maximum Likelihood Technique

AbstractSample sizes needed to measure population levels of the birch casebearer, Coleophora fuscedinella Zeller, and its damage to white birch, Betula papyrifera Marsh, were calculated for various degrees of confidence and assurance. Both a non-destructive and a destructive sampling plan are presented and a new method to classify the damage level of a stand. This method uses a maximum likelihood technique to estimate the proportion of trees of various damage classes.

scholarly journals Fitting Wind Speed to a 3-Parameter Distribution Using Maximum Likelihood Technique

2021 ◽  
Author(s):  
Benedict Troon
Keyword(s):  
Maximum Likelihood ◽  
Wind Speed ◽  
Gamma Distribution ◽  
Model Comparison ◽  
Information Criterion ◽  
Parameter Distribution ◽  
Wind Speed Data ◽  
Distribution Parameters ◽  
Total Capacity ◽  
Maximum Likelihood Technique

Kenya is one of the countries in the world with a good quantity of wind. This makes the country to work ontechnologies that can help in harnessing the wind with a vision of achieving a total capacity of 2GW of wind energy by 2030.The objective of this research is to find the best three-parameter wind speed distribution for examining wind speed using the maximum likelihood fitting technique. To achieve the objective, the study used hourly wind speed data collected for a period of three years (2016 – 2018) from five sites within Narok County. The study examines the best distributions that the data fits and then conducted a suitability test of the distributions using the Kolmogorov-Smirnov test. The distribution parameters were fitted using maximum likelihood technique and model comparison test conducted using Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values with the decision rule that the best distribution relies on the distribution with the smaller AIC and BIC values. The research showed that the best distribution is the gamma distribution with the shape parameter of 2.071773, scale parameter of 1.120855, and threshold parameter of 0.1174. A conclusion that gamma distribution is the best three-parameter distribution for examining the Narok country wind speed data

A GENERAL MULTI-STATE START-UP DEMONSTRATION TESTING PROCEDURE

2013 ◽  
Vol 20 (04) ◽  
pp. 1350013 ◽  
Author(s):  
AMOS E. GERA
Keyword(s):  
Maximum Likelihood ◽  
Constrained Optimization ◽  
Correct Choice ◽  
Testing Procedure ◽  
Design Parameters ◽  
Expected Number ◽  
State Tests ◽  
Start Up ◽  
Degradation Failure ◽  
Maximum Likelihood Technique

The recently introduced TSCSTFCF start-up demonstration procedure is generalized to include multi-state tests (success, degradation, failure). The expected number of required tests and the probability of acceptance are evaluated. Comparisons to the previously known multi-state TSCS, CSTF and CSCF procedures, and to the binary TSCSTFCF procedure with i.i.d. tests are provided. A constrained optimization is presented for reducing the expected number of required tests subject to some constraints. Some reduction in the total number of tests is achieved for a correct choice of the relevant design parameters. Also, a procedure for estimating the possibly unknown probabilities of success, degradation and failure is presented using a maximum likelihood technique. Finally, it is pointed out how to extend the analysis for the case of nonidentical units.

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