Theory of Partial Likelihood

Partial likelihood for signal processing

We present partial likelihood (PL) as an effective means for developing nonlinear techniques for signal processing. Posing signal processing problems in a likelihood setting provides a number of advantages, such as allowing the use of powerful tools in statistics and easy incorporation of model order/complexity selection into the problem by use of appropriate information-theoretic criteria. How...

Partial likelihood and estimating equations

Consider a regression model for discrete-time stochastic processes, with a (partially speciied) model for the conditional distribution of the response given the covariate and the past observations. Suppose we also have some knowledge about how the parameter of interest aaects the conditional distribution of the covariate given the past. We assume that these two model assumptions give rise to tw...

Fuzzy type theory with partial functions

This paper is a study of fuzzy type theory (FTT) with partial functions. Out of several possibilities we decided tointroduce a special value ”∗” that represents ”undefined”. In the interpretation of FTT, this value lays outside of thecorresponding domain. In the syntax it can be naturally represented by the description operator acting on the empty(fuzzy) set, because choosing an element from it...

Local Likelihood and Local Partial Likelihood in Hazard Regression

Projected partial likelihood and its application

SUMMARY An estimating equation, which we call the projected partial score, is introduced for longitudinal data analysis. The estimating equation is obtained by projecting the partial likelihood score function onto the vector space spanned by a class of \conditionally linear" estimating equations. We demonstrate that removing certain terms from the projection of the full likelihood score does no...