This paper is concerned with the derivation of new estimators and performance bounds for the problem of timing estimation of (linearly) digitally modulated signals. The conditional maximum likelihood (CML) method is adopted, in contrast to the classical low-SNR unconditional ML (UML) formulation that is systematically applied in the literature for the derivation of non-data-aided (NDA) timing-e...

In this paper we propose a new Sieve-based Locally Weighted Conditional Empirical Likelihood (SLWCEL) estimator for models of conditional moment restrictions containing …nite dimensional unknown parameters and in…nite dimensional unknown functions h. The SLWCEL is a one-step information-theoretic alternative to the Sieve Minimum Distance estimator analyzed by Ai and Chen (2003). We approximate ...

Disease prevalence is the combined result of duration, disease incidence, case fatality, and other mortality. If information is available on all these factors, and on fixed covariates such as genotypes, prevalence information can be utilized in the estimation of the effects of the covariates on disease incidence. Study cohorts that are recruited as cross-sectional samples and subsequently follo...

‘Iterative conditional fitting’ is a recently proposed algorithm that can be used for maximization of the likelihood function in marginal independence models for categorical data. This paper describes a modification of this algorithm, which allows one to compute maximum likelihood estimates in a class of chain graph models for categorical data. The considered discrete chain graph models are def...

The asymptotic distribution of maximum likelihood estimators is derived for a class of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models. The result carries over to models for duration and realised volatility that use an exponential link function. A key feature of the model formulation is that the dynamics are driven by the score. Keywords: Duration models; g...

The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithm. The minimum classification error (MCE) method and the soft nearest prototype classifier (SNPC) method are two important algorithms using misclassification loss. This paper proposes a new prototype learning algorithm based on the conditional log-likelihood loss (CLL), which is base...

In this technical report, we consider conditional density estimation with a maximum likelihood approach. Under weak assumptions, we obtain a theoretical bound for a KullbackLeibler type loss for a single model maximum likelihood estimate. We use a penalized model selection technique to select a best model within a collection. We give a general condition on penalty choice that leads to oracle ty...

In this paper we discuss a method, which we call Minimum Conditional Description Length (MCDL), for estimating the parameters of a subset of sites within a Markov random field. We assume that the edges are known for the entire graph G = (V,E). Then, for a subset U ⊂ V , we estimate the parameters for nodes and edges in U as well as for edges incident to a node in U , by finding the exponential ...

1 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada H3A 1A2 2 Indic Society for Education and Development (INSEED), Nashik, Maharashtra 422 011, India 3 Department of Vaccines, National Institute for Health and Welfare, 00271 Helsinki, Finland 4 Department of Mathematics and Statistics, University of Tampere, 33014 Tampere, Finland 5 Depa...

1 We present a conditional density model of river runoff given covariate information 2 which includes precipation at four surrounding stations. The proposed model is non3 parametric in the central part of the distribution and relies on Extreme-Value Theory 4 parametric assumptions for the upper tail of the distribution. From the trained con5 ditional density model, we can compute quantiles of v...