14,233 research outputs found
Sparse Regression with Multi-type Regularized Feature Modeling
Within the statistical and machine learning literature, regularization
techniques are often used to construct sparse (predictive) models. Most
regularization strategies only work for data where all predictors are treated
identically, such as Lasso regression for (continuous) predictors treated as
linear effects. However, many predictive problems involve different types of
predictors and require a tailored regularization term. We propose a multi-type
Lasso penalty that acts on the objective function as a sum of subpenalties, one
for each type of predictor. As such, we allow for predictor selection and level
fusion within a predictor in a data-driven way, simultaneous with the parameter
estimation process. We develop a new estimation strategy for convex predictive
models with this multi-type penalty. Using the theory of proximal operators,
our estimation procedure is computationally efficient, partitioning the overall
optimization problem into easier to solve subproblems, specific for each
predictor type and its associated penalty. Earlier research applies
approximations to non-differentiable penalties to solve the optimization
problem. The proposed SMuRF algorithm removes the need for approximations and
achieves a higher accuracy and computational efficiency. This is demonstrated
with an extensive simulation study and the analysis of a case-study on
insurance pricing analytics
Regularization and Model Selection with Categorial Effect Modifiers
The case of continuous effect modifiers in varying-coefficient models has been well investigated. Categorial effect modifiers, however, have been largely neglected. In this paper a regularization technique is proposed that allows for selection of covariates and fusion of categories of categorial effect modifiers in a linear model. It is distinguished between nominal and ordinal variables, since for the latter more economic parametrizations are warranted. The proposed methods are illustrated and investigated in simulation studies and real world data evaluations. Moreover, some asymptotic properties are derived
HOW TO GROUP MARKET PARTICIPANTS? HETEROGENEITY IN HEDGING BEHAVIOR
Using a generalized mixture model, we model individual heterogeneity by identifying groups of participants that respond in a similar manner to the determinants of economic behavior. The procedure emphasizes the role of theory as the determinants of behavior are used to simultaneously explain market activities and to discriminate among groups of market participants. We show the appealing properties of this modeling approach by comparing it with two often used grouping methods in an empirical study in which we estimate the factors affecting market participants' hedging behavior.Institutional and Behavioral Economics,
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