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Bayesian Estimation and Model Selection for the Spatiotemporal Autoregressive Model with Autoregressive Conditional Heteroscedasticity Errors
The spatial and spatiotemporal autoregressive conditional heteroscedasticity (STARCH) models receive increasing attention. In this paper, we...
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GMM estimation and variable selection of partially linear additive spatial autoregressive model
The generalized method of moments (GMM) has been recognized as a particularly popular estimation procedure in terms of computational simplicity and...
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Solar radiation estimation using ANFIS model: evaluation of membership function types and data selection
This study proposed a model for estimating monthly solar radiation values using the adaptive network-based fuzzy inference systems (ANFIS-SR). The...
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DS-HECK: double-lasso estimation of Heckman selection model
We extend the Heckman (1979) sample selection model by allowing for a large number of controls that are selected using lasso under a sparsity...
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Improving a Model for NFR Estimation Using Band Classification and Selection with KNN
AbstractAny software development project needs to estimate non-functional requirements (NFR). Typically, software managers are forced to use expert...
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Model Selection
This chapter shows an application of the MDL principle to statistical model selection. First a number of existing model selection criteria such as...
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Local Walsh-average-based Estimation and Variable Selection for Spatial Single-index Autoregressive Models
This paper is concerned with spatial single-index autoregressive model (SSIM), where the spatial lag effect enters the model linearly and the...
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An adaptive identification method for outliers in dam deformation monitoring data based on Bayesian model selection and least trimmed squares estimation
An important technique for the quantitative analysis of dam deformation state is to establish safety monitoring models using deformation monitoring...
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Model Validation and Selection
This chapter addresses the fundamental aspects of model validation and selection in the field of machine learning. It begins by discussing the...
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Model Selection and Regularization
This chapter presents regularization and selection methods for linear and nonlinear (parametric)Parametric models. These are important Machine...
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Mutual information-based neighbor selection method for causal effect estimation
Estimation of causal effects from observational data has been the main objective in several high-impact scientific domains, while the golden standard...
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Bayesian active learning with model selection for spectral experiments
Active learning is a common approach to improve the efficiency of spectral experiments. Model selection from the candidates and parameter estimation...
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Efficient estimation and correction of selection-induced bias with order statistics
Model selection aims to identify a sufficiently well performing model that is possibly simpler than the most complex model among a pool of...
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Model Evaluation and Selection
Estimating the performance of a constructed predictive model, also known as model evaluation, is of essential importance in machine learning. This is...
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Ellipsoidal buffered area under the curve maximization model with variable selection in credit risk estimation
In 2019, a buffered AUC (bAUC) maximization model with the linear classifier was developed to maximize the area under the curve (AUC), a popular...
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Communication-efficient estimation for distributed subset selection
Due to the large scale both of the sample size and dimensions, modern data is usually stored in a distributed system, which poses unprecedented...
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Model Selection
In this chapter, we discuss approaches for a problem called model selection. Model selection is always needed when there are a number of candidate...
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Multiple Model Estimation for Nonlinear Classification
This chapter describes a new method for nonlinear classification using a collection of several simple (linear) classifiers. The approach is based on...
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Robust model estimation by using preference analysis and information theory principles
Robust model estimation aims to estimate the parameters of a given geometric model, and then separate the outliers and inliers belonging to different...
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An adaptive boundary-based selection many-objective evolutionary algorithm with density estimation
Many-objective evolutionary algorithms often struggle to strike a balance between convergence and diversity when solving many-objective optimization...
