Estimation of gillnet and hook selectivity using log-linear models

Estimation of Gillnet Selectivity Curve by Maximum Likelihood Method

Maximum Likelihood Estimation in Log - Linear Models

We study maximum likelihood estimation in log-linear models under conditional Poisson sampling schemes. We derive necessary and sufficient conditions for existence of the maximum likelihood estimator (MLE) of the model parameters and investigate estimability of the natural and mean-value parameters under a non-existent MLE. Our conditions focus on the role of sampling zeros in the observed tabl...

Decomposable Log-linear Models Decomposable Log-linear Models

The present paper considers discrete probability models with exact computational properties. In relation to contingency tables this means closed form expressions of the maksimum likelihood estimate and its distribution. The model class includes what is known as decomposable graphical models, which can be characterized by a structured set of conditional independencies between some variables give...

Computationally Efficient M-Estimation of Log-Linear Structure Models

We describe a new loss function, due to Jeon and Lin (2006), for estimating structured log-linear models on arbitrary features. The loss function can be seen as a (generative) alternative to maximum likelihood estimation with an interesting information-theoretic interpretation, and it is statistically consistent. It is substantially faster than maximum (conditional) likelihood estimation of con...

Contrastive Estimation: Training Log-Linear Models on Unlabeled Data

Conditional random fields (Lafferty et al., 2001) are quite effective at sequence labeling tasks like shallow parsing (Sha and Pereira, 2003) and namedentity extraction (McCallum and Li, 2003). CRFs are log-linear, allowing the incorporation of arbitrary features into the model. To train on unlabeled data, we require unsupervised estimation methods for log-linear models; few exist. We describe ...