An explicit procedure is given to obtain the exact maximum likelihood estimates of the parameters in a regression model with ARMA time series errors with possibly nonconsecutive data. The method is ...

A random sample of curves can be usually thought of as noisy realisations of a compound stochastic process X(t) = Z{W(t)}, where Z(t) produces random amplitude variation and W(t) produces random ...

Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

Nonlinear estimation algorithms are required for obtaining estimates of the parameters of a regression model with innovations having an ARMA structure. The three estimation methods employed by the ...

The challenge of using small sample sizes for operational risk capital models fitted via maximum likelihood estimation is well recognized, yet the literature generally provides warning examples rather ...

The following data are taken from Lawless (1982, p.193) and represent the number of days it took rats painted with a carcinogen to develop carcinoma. The last 2 observations are censored data from a ...

Our method can be used to train implicit probabilistic models (a common example being the generator in GANs). Unlike GANs, however, our method does not suffer from mode collapse/dropping and is stable ...

In the process of loan pricing, stress testing, capital allocation, modeling of probability of default (PD) term structure and International Financial Reporting Standard 9 expected credit loss ...