Bregman-divergence-guided Legendre exponential dispersion model with finite cumulants (K-LED)
This work provides a more flexible framework for statistical modeling in machine learning, though it appears incremental as it builds on existing exponential dispersion models.
The authors tackled the restrictive conditions of exponential dispersion models by introducing K-LED, a relaxed model with finite cumulants, enabling easier computation of mean parameters and equivalence to quasi-likelihood functions.
Exponential dispersion model is a useful framework in machine learning and statistics. Primarily, thanks to the additive structure of the model, it can be achieved without difficulty to estimate parameters including mean. However, tight conditions on cumulant function, such as analyticity, strict convexity, and steepness, reduce the class of exponential dispersion model. In this work, we present relaxed exponential dispersion model K-LED (Legendre exponential dispersion model with K cumulants). The cumulant function of the proposed model is a convex function of Legendre type having continuous partial derivatives of K-th order on the interior of a convex domain. Most of the K-LED models are developed via Bregman-divergence-guided log-concave density function with coercivity shape constraints. The main advantage of the proposed model is that the first cumulant (or the mean parameter space) of the 1-LED model is easily computed through the extended global optimum property of Bregman divergence. An extended normal distribution is introduced as an example of 1-LED based on Tweedie distribution. On top of that, we present 2-LED satisfying mean-variance relation of quasi-likelihood function. There is an equivalence between a subclass of quasi-likelihood function and a regular 2-LED model, of which the canonical parameter space is open. A typical example is a regular 2-LED model with power variance function, i.e., a variance is in proportion to the power of the mean of observations. This model is equivalent to a subclass of beta-divergence (or a subclass of quasi-likelihood function with power variance function). Furthermore, a new parameterized K-LED model, the cumulant function of which is the convex extended logistic loss function, is proposed. This model includes Bernoulli distribution and Poisson distribution.