Analysis of breast cancer data in framework of a GPD model with interval censoring


In this work, we are interested in a hypothesis testing problem within the
framework of a GPD model with interval censoring. For this purpose, we rst
develop the calculation of the likelihood function using conditional probabilities to achieve the same expression proposed by Klein and Moeschberger. Next, we show that the properties of the maximum pseudo-likelihood estimates of the model parameters, and essentially the asymptotic normality, are preserved. Finally, we built a hypothesis testing to compare two types of breast cancer treatment as part of the model mentioned above.

DOI Code: 10.1285/i20705948v12n2p380

Keywords: Interval censoring; likelihood function; GPD model; asymptotic normality; hypothesis testing


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