We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood is maximized but the assumption of normality is violated. Because the score of the normal log-likelihood has the martingale difference property when the first two conditional m...

Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised learning algorithms have been successfully adapted for the multiple-instance learning settings. We explore the adaptation of the Naive Bayes (NB) classifier and the utilization of its sufficient statistics for develop...

The use of spectrally e cient continuous phase modulations for mobile communications may lead to a serious performance degradation of the classical frequency error detectors (FEDs) due to the presence of self-noise. This contribution presents a new statistically e cient frequency estimation algorithm for staggered modulations. The cancellation of the self-noise is accomplished by the use of the...

The performance of an extended Kalman iilter (EKE') applied to the problem of estimating the (assumed constant) parameten (fundamental frequency, harmonic phases, and amplitudes) of a complex multiharmonic signal measured in noise is shown to he asymptotically (i.e., as the number of measurements tends to infinity) efliuent. The Cramer-Rao (CR) bounds associated with the estimation problem are ...

We introduce predictive random fields, a framework for learning undirected graphical models based not on joint, generative likelihood, or on conditional likelihood, but based on a product of several conditional likelihoods each relying on common sets of parameters and predicting different subsets of variables conditioned on other subsets. When applied to models with latent variables, such as th...

Abstract: Parameter estimation in a class of heteroscedastic time series models is investigated. The existence of conditional least-squares and conditional likelihood estimators is proved. Their consistency and their asymptotic normality are established. Kernel estimators of the noise’s density and its derivatives are defined and shown to be uniformly consistent. A simulation experiment conduct...

The phylogenetic likelihood function (PLF) is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection, and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. V...

The problem of labeling (or segmenting) sequences is very important in many applications such as part-of-speech tagging in natural language processing, multimodal object detection in computer vision, and DNA/protein structure prediction in bioinformatics. Conditional Random Fields (CRFs) of [1] are known to be the best sequence models ever for the problem. CRF is a conditional model, P (s|y), i...

Markov processes are used in a wide range of disciplines including finance. The transitional densities of these processes are often unknown. However, the conditional characteristic functions are more likely to be available especially for Lévy driven processes. We propose an empirical likelihood approach for estimation and model specification test based on the conditional characteristic function...