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Parameter Estimation Of The Mixed Generalized Gamma Distribution Using Maximum Likelihood Estimation And Minimum Distance Estimation, Dean G. Boerrigter
Parameter Estimation Of The Mixed Generalized Gamma Distribution Using Maximum Likelihood Estimation And Minimum Distance Estimation, Dean G. Boerrigter
Theses and Dissertations
The Generalized Gamma is an extremely flexible distribution that is useful for reliability modeling. Among its many special cases are the Weibull and Exponential distributions. A mixture of Generalized Gamma Distributions is even more useful because multiple causes of failure can he simultaneously modeled. This research studied parameter estimation of the special cases of the Mixed Generalized Gamma Distribution and built upon them until the full nine-parameter distribution was being estimated. First, special cases of a single Generalized Gamma Distribution were estimated. Next, mixtures of Exponential distributions with both known and unknown location parameters were estimated. Next, mixtures of Weibull …
A New Sequential Goodness Of Fit Test For The Three-Parameter Weibull Distribution With Known Shape Based On Skewness And Kurtosis, Jonathan C. Clough
A New Sequential Goodness Of Fit Test For The Three-Parameter Weibull Distribution With Known Shape Based On Skewness And Kurtosis, Jonathan C. Clough
Theses and Dissertations
The Weibull distribution finds wide applicability across a broad spectrum of disciplines and is very prevalent in reliability theory. Consequently, numerous statistical tests have been developed to determine whether sample data can be adequately modeled with this distribution. Unfortunately, the majority of these goodness-of-fit tests involve a substantial degree of computational complexity. The study presented here develops and evaluates a new sequential goodness-of-fit test for the three-parameter Weibull distribution with a known shape that delivers power comparable to popular procedures while dramatically reducing computational requirements. The new procedure consists of two distinct tests, using only the sample skewness and sample …
Anderson-Darling And Cramer-Von Mises Based Goodness-Of-Fit Tests For The Weibull Distribution With Known Shape Using Normalized Spacings, Eric W. Frisco
Anderson-Darling And Cramer-Von Mises Based Goodness-Of-Fit Tests For The Weibull Distribution With Known Shape Using Normalized Spacings, Eric W. Frisco
Theses and Dissertations
Two new goodness-of-fit tests are developed for the three-parameter Weibull distribution with known shape parameter. These procedures eliminate the need for estimating the unknown location and scale parameters prior to initiating the tests and are easily adapted for censored data. This is accomplished by employing the Anderson-Darling and Cramer-von Mises statistics based on the normalized spacings of the sample data. Critical values of the new tests are obtained for common significance levels by large Monte Carlo simulations for shapes 0.5(0.5)4.0 and sample sizes 5(5)40 with up to 40% censoring (Type II) from the left and/or right. An extensive Monte Carlo …
Orbit Estimation Using Track Compression And Least Squares Differential Correction, Vincent J. Chioma
Orbit Estimation Using Track Compression And Least Squares Differential Correction, Vincent J. Chioma
Theses and Dissertations
This thesis develops two methods of compressing a track of radar observations of a satellite into a single state vector and associated covariance matrix, and a method of estimating orbits using results from multiple tracks. The track compression uses least squares differential correction to determine a state vector at the central observation time. The resulting state vectors and covariance matrices are then used to estimate the satellite's orbit, also using least squares differential correction. Numerical integration using two-body, J2 and an atmospheric drag model is used to represent the dynamics. This orbit estimation produces a state vector which includes …
Parameter Estimation For Real Filtered Sinusoids, Daniel R. Zahirniak
Parameter Estimation For Real Filtered Sinusoids, Daniel R. Zahirniak
Theses and Dissertations
This research develops theoretical methods for parameter estimation of filtered, pulsed sinusoids in noise and demonstrates their effectiveness for Electronic Warfare EW applications. Within the context of stochastic modeling, a new linear model, parameterized by a set of Linear Prediction LP coefficients, is derived for estimating the frequencies of filtered sinusoids. This model is an improvement over previous modeling techniques since the effects of the filter and the coefficients upon the noise statistics are properly accounted for during model development. From this linear model, a relationship between LP coefficient estimation and Maximum Likelihood ML frequency estimation is derived and several …