Estimation from a censored sample for the exponential family
BiometrikaPublished 1 January 1970
B. J. N. Blight
Citations31
SJR quartileQ1
SJR score3.60
SNIP2.67
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Abstract
The likelihood equations are derived for the estimation of the parameters of an exponential family from a Type I censored sample and are shown to have an interpretation which suggests a particular iterative method of solution. Some results relevant to the convergence properties of this method are given and the asymptotic variance-covariance matrix is derived. The method is illustrated by an example in which it is compared with an alternative method in the case of estimation for a doubly censored normal distribution.
Keywords
MathematicsDecision Sciences
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It is usually assumed in linear regression theory that for a given value of the regressor variable t, the dependent variable y is distributed normally with expectation a linear function of t, say y N(a + bt, c-) However, situations arise in practice where the distribution of y must necessarily be truncated at a value independent of t.