Parameter Estimation for the Bivariate Exponential Distribution by the EM Algorithm Based on Censored Samples
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David D. Hanagal
Abstract
In this paper, we estimate the parameters using the EM algorithm of the bivariate exponential distribution of Marshall-Olkin when the samples are right censored. The advantage of using the EM algorithm is that the observed data vector is viewed as being incomplete but regarded as an observable function of complete data. Then the EM algorithm exploits the reduced complexity of maximum likelihood estimation for complete data. We also derive the standard deviations of the estimates for this bivariate exponential distribution. A simulation study is conducted to compare the estimated values with the true values. It turns out that the estimates based on EM algorithm are more better than estimates obtained without the EM algorithm.
© Heldermann Verlag
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- Parameter Estimation for the Bivariate Exponential Distribution by the EM Algorithm Based on Censored Samples
- Explicit Expressions for Moments of Log Normal Order Statistics
- Partially Specified Prior
- The Need for a Standard for Making Predictions
Articles in the same Issue
- Stochastic Measurement Procedures Based on Stationary Time Series
- Semi-Parametric Estimation of PX,Y (X > Y)
- On The Performance of A New Test of Exponentiality Against IFR Alternatives Based on the L-statistic Approach
- Control Chart for Autocorrelated Processes with Heavy Tailed Distributions
- Designing the Scale Counting Procedure for Large Numbers of Small Parts
- MTBF for K-out-of-N: G Systems with M Failure Modes
- Non-parametric Control Chart for Controlling Variability Based on Rank Test
- Bounds for Distorted Risk Measures
- Parameter Estimation for the Bivariate Exponential Distribution by the EM Algorithm Based on Censored Samples
- Explicit Expressions for Moments of Log Normal Order Statistics
- Partially Specified Prior
- The Need for a Standard for Making Predictions
