Add to ORKGMaximum likelihood estimation of parameters is considered in the situation where a measurement x is taken to mean x + d. The maximum likelihood estimator for the parameter of the exponential distribution is found for this case and compared with the usual estimator
When a p-dimensional parameter θ is defined through the moment condition Em(X,θ) = 0, a simple estima...
Graduation date: 2006Regression calibration inference seeks to estimate regression models with measu...
How to estimate parameters from observations subject to errors and uncertainty? Very often, the meas...
Maximum likelihood estimation of parameters is considered in the situation where a measurement x is ...
A maximum likelihood (ML) estimation procedure is developed to find the mean of the exponential fami...
We propose novel estimators for the parameters of an exponential distribution and a normal distribut...
We propose novel estimators for the parameters of an exponential distribution and a normal distribut...
This paper describes an EM algorithm for maximum likelihood estimation in generalized linear models ...
We propose novel estimators for the parameters of an exponential distribution and a normal distribut...
The exponential distribution is commonly used to model the behavior of units that have a constant fa...
International audienceConsider an autoregressive model with measurement error: we observe $Z_i=X_i+\...
The maximum likelihood estimation of the unknown parameters of inverse Rayleigh and exponential dist...
The exponential distribution is commonly used to model the behavior of units that have a constant fa...
Maximum likelihood is by far the most pop-ular general method of estimation. Its wide-spread accepta...
The importance of measurement error for parameter estimation and for the design of statistical studi...
When a p-dimensional parameter θ is defined through the moment condition Em(X,θ) = 0, a simple estima...
Graduation date: 2006Regression calibration inference seeks to estimate regression models with measu...
How to estimate parameters from observations subject to errors and uncertainty? Very often, the meas...
Maximum likelihood estimation of parameters is considered in the situation where a measurement x is ...
A maximum likelihood (ML) estimation procedure is developed to find the mean of the exponential fami...
We propose novel estimators for the parameters of an exponential distribution and a normal distribut...
We propose novel estimators for the parameters of an exponential distribution and a normal distribut...
This paper describes an EM algorithm for maximum likelihood estimation in generalized linear models ...
We propose novel estimators for the parameters of an exponential distribution and a normal distribut...
The exponential distribution is commonly used to model the behavior of units that have a constant fa...
International audienceConsider an autoregressive model with measurement error: we observe $Z_i=X_i+\...
The maximum likelihood estimation of the unknown parameters of inverse Rayleigh and exponential dist...
The exponential distribution is commonly used to model the behavior of units that have a constant fa...
Maximum likelihood is by far the most pop-ular general method of estimation. Its wide-spread accepta...
The importance of measurement error for parameter estimation and for the design of statistical studi...
When a p-dimensional parameter θ is defined through the moment condition Em(X,θ) = 0, a simple estima...
Graduation date: 2006Regression calibration inference seeks to estimate regression models with measu...
How to estimate parameters from observations subject to errors and uncertainty? Very often, the meas...