Regularized M-estimators are used in diverse areas of science and engineering to fit high-dimensional models with some low-dimensional structure. Usually the low-dimensional structure is encoded by the presence of the (unknown) parameters in some low-dimensional model subspace. In such settings, it is desirable for est…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
Trend · papers per month
48 results for “consistent model selection”
Lasso proves consistent model selection for high-dimensional Ising models.
problem Model selection consistency of Lasso for high-dimensional Ising models.
method Theoretical analysis of Lasso with and without post-thresholding for Ising models.
result Lasso without post-thresholding is model selection consistent in the whole paramagnetic phase with .
Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produced by a regularization algorithm, a consistent model selection criterion is proposed to select the best one among this preselected set. The a…
New method proves neural networks can select features consistently.
problem Feature selection for deep neural networks is challenging.
method Adaptive Group Lasso selection procedure with Group Lasso as the base estimator.
result Adaptive Group Lasso is selection-consistent for a wide class of neural networks.
We consider selection of random predictors for high-dimensional regression problem with binary response for a general loss function. Important special case is when the binary model is semiparametric and the response function is misspecified under parametric model fit. Selection for such a scenario aims at recovering th…
Consistent model selection for spiked Wigner model via AIC-type criteria.
problem Estimating the number of spiked eigenvalues in the spiked Wigner model.
method AIC-type model selection criteria with parameters γ.
result Strong consistency for γ > 2 and weak consistency for γ = 2 + δ_N.
The paper examines how insurers can select claims for fraud investigation, proposing a randomized approach.
problem Inconsistent learning from biased claim selection.
method Formalizes selection in binary regression, proposes a randomized alternative, and defines consistency.
result The randomized selection strategy is consistent, while the traditional strategy is not.
We consider the least-square linear regression problem with regularization by the -norm, a problem usually referred to as the Lasso. In this paper, we first present a detailed asymptotic analysis of model consistency of the Lasso in low-dimensional settings. For various decays of the regularization parameter, w…
Model selection is difficult to analyse yet theoretically and empirically important, especially for high-dimensional data analysis. Recently the least absolute shrinkage and selection operator (Lasso) has been applied in the statistical and econometric literature. Consis- tency of Lasso has been established under vario…
We consider the least-square linear regression problem with regularization by the l1-norm, a problem usually referred to as the Lasso. In this paper, we present a detailed asymptotic analysis of model consistency of the Lasso. For various decays of the regularization parameter, we compute asymptotic equivalents of the …
Gaussian OBFS proves strong consistency in feature selection with correlations.
problem Feature selection consistency in the presence of correlations.
method Proves strong consistency of Gaussian OBFS under mild conditions.
result Identifies selected features and rates of convergence for different feature types.
A Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Randomstat.ME
Unified framework for variable selection in model-based clustering with missing data.
problem Challenges in identifying relevant variables and handling missing data in model-based clustering.
method Unified framework incorporating a data-driven penalty matrix and a mechanism for missingness modeling.
result Achieves both asymptotic consistency and selection consistency in the presence of missing data.
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature, their performances largely depend on the tuning parameters that balance the trade-off between model fitting and model s…
ARGEN method improves variable selection and regularization in high-dimensional sparse models.
problem Constrained variable selection and regularization in high-dimensional sparse linear models.
method ARGEN penalty method, variable selection and regularization.
result ARGEN method has variable selection and estimation consistency under certain conditions.
Improved selection of best outputs from LLMs for better accuracy.
problem BoN fails to reliably find the correct answer with imperfect rewards.
method Majority-of-the-Bests (MoB) selects the mode of a bootstrapped output distribution of BoN.
result MoB consistently improves over BoN in 25 out of 30 setups.
New algorithm finds best subset in high-dimensional data models.
problem Finding the best subset of predictors in high-dimensional data models.
method Proposes a scalable algorithm using a generalized information criterion.
result Directly proves consistency and oracle property for the best-subset selection.
We introduce a computationally effective algorithm for a linear model selection consisting of three steps: screening--ordering--selection (SOS). Screening of predictors is based on the thresholded Lasso that is l_1 penalized least squares. The screened predictors are then fitted using least squares (LS) and ordered wit…
Variable screening is a fast dimension reduction technique for assisting high dimensional feature selection. As a preselection method, it selects a moderate size subset of candidate variables for further refining via feature selection to produce the final model. The performance of variable screening depends on both com…
High signal to noise ratio (SNR) consistency of model selection criteria in linear regression models has attracted a lot of attention recently. However, most of the existing literature on high SNR consistency deals with model order selection. Further, the limited literature available on the high SNR consistency of subs…
Model selection is crucial to high-dimensional learning and inference for contemporary big data applications in pinpointing the best set of covariates among a sequence of candidate interpretable models. Most existing work assumes implicitly that the models are correctly specified or have fixed dimensionality. Yet both …
Adaptive Group Lasso selects important features in neural networks.
problem Lack of interpretability in neural networks.
method Adaptive Group Lasso for feature selection.
result Consistent feature selection for neural networks with theoretical guarantee.
We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises when historical decision making blinds us to the true outcome for certain instances. Examples of this are common in many applications, rangin…
FSinR is a comprehensive R package for feature selection.
problem Feature selection in machine learning.
method Filter and wrapper methods, search algorithms.
result Provides a variety of feature selection methods and search algorithms.
Corrects a mistake in -block cross-validation consistency claim.
problem Incorrectly assumed -block cross-validation is a BIBD.
method Demonstrates -block is not a BIBD and reopens consistency question.
result Theoretical consistency of -block remains unresolved.
Determining how to appropriately select the tuning parameter is essential in penalized likelihood methods for high-dimensional data analysis. We examine this problem in the setting of penalized likelihood methods for generalized linear models, where the dimensionality of covariates p is allowed to increase exponentiall…
Proposes a method for stable variable selection in high-dimensional data.
problem Challenges of variable selection in high-dimensional, correlated data.
method Resample-aggregate framework using diffusion models.
result Stable subset of predictors with calibrated stability scores.
Recently, many regularized procedures have been proposed for variable selection in linear regression, but their performance depends on the tuning parameter selection. Here a criterion for the tuning parameter selection is proposed, which combines the strength of both stability selection and cross-validation and therefo…
A new feature selection method using random forest and Kolmogorov filter.
problem Ultra-high dimensional data feature selection.
method Fused Kolmogorov filter with random forest based recursive feature elimination.
result Selection and consistency under weak conditions.
MOMENT selects and estimates mixed-effects models using moment identities.
problem Selecting and estimating random-effects covariance matrix and fixed-effects coefficients in multiresponse linear mixed-effects models.
method MOMENT is a stage-wise moment-based framework that reduces the random-effects selection problem to a smooth constrained convex optimization problem.
result MOMENT performs competitively and can outperform separate univariate analyses for correlated responses.
Paper develops a consistent model selection framework for learning Hypotheses Space from data.
problem Avoiding overfitting in complex spaces with limited data.
method Develops a model selection framework based on Learning Spaces, selecting a Hypotheses Space from data.
result The method converges with probability one to a target Hypotheses Space, providing a consistent framework for model selection.
In this paper, we propose a variable selection method for general nonparametric kernel-based estimation. The proposed method consists of two-stage estimation: (1) construct a consistent estimator of the target function, (2) approximate the estimator using a few variables by l1-type penalized estimation. We see that the…
A new family of penalty functions, adaptive to likelihood, is introduced for model selection in general regression models. It arises naturally through assuming certain types of prior distribution on the regression parameters. To study stability properties of the penalized maximum likelihood estimator, two types of asym…
Conformal Prediction Assessment: A Framework for Conditional Coverage Evaluation and Selectionstat.ME
CPA framework assesses conditional validity of conformal prediction.
problem Challenges in evaluating conditional validity of conformal prediction.
method Reframes conditional coverage evaluation as a supervised learning task.
result Establishes convergence rates and proves CVI consistency.
Causal graphs, such as directed acyclic graphs (DAGs) and partial ancestral graphs (PAGs), represent causal relationships among variables in a model. Methods exist for learning DAGs and PAGs from data and for converting DAGs to PAGs. However, these methods are significantly limited in that they only output a single cau…
Study finds AUC is most consistent across different prevalence in binary classification.
problem Consistency of model evaluation metrics across varying prevalence in binary classification.
method Analysis of 156 data scenarios with 18 metrics, 5 models, and a random guess model.
result AUC has the smallest variance in evaluating individual models and ranking of models.
Nonparametric undirected graphical model selection using diffusion models
problem Undirected graphical model selection
method Diffusion models
result Model selection consistency
Dimension reduction and variable selection are performed routinely in case-control studies, but the literature on the theoretical aspects of the resulting estimates is scarce. We bring our contribution to this literature by studying estimators obtained via L1 penalized likelihood optimization. We show that the optimize…
Proposes a method to learn sparse deep neural networks with theoretical guarantees.
problem Over-parameterized deep neural networks cause training, prediction, and interpretation difficulties.
method Frequentist-like method for sparse DNNs under Bayesian framework.
result Consistent sparse DNNs with at most connections.
This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models. The proposed method is shown to be statistically consistent in determining of th…
Targeted Cross-Validationstat.ML
Proposes TCV for selecting models in regions of interest.
problem Selecting models for specific regions of predictor variables.
method Targeted Cross-Validation (TCV) using weighted L2 loss.
result TCV is consistent in selecting the best performing candidate under weighted L2 loss.
Proposes FarmHazard model for hazard regression with correlated covariates.
problem Model selection challenges in high-dimensional data with correlated covariates.
method Factor-Augmented Regularized Model for Hazard Regression (FarmHazard) that learns latent factors and idiosyncratic components.
result Proves model selection and estimation consistency under mild conditions.
Gini index needs auto-calibration for consistent decision-making.
problem Gini index's inconsistency in decision-making.
method Restrict Gini index to auto-calibrated regression models.
result Gini index becomes strictly consistent with auto-calibration.
Beam search improves feature selection for better model performance.
problem Improving feature selection for better model performance.
method Proposed beam search as a generalization of forward selection for feature selection.
result Beam search can outperform forward selection, especially with correlated features.
Develops a forward variable selection method for interpretable random forest models.
problem Interpreting high-dimensional non-parametric models like random forests.
method Forward variable selection using CRPS as loss function, with hypothesis testing at each step.
result Method selects a smaller set of variables that optimizes predictive performance.
LLMs can select predictive features without seeing training data.
problem Selecting the best features for prediction tasks.
method Zero-shot prompting LLMs with feature names and task descriptions.
result LLMs consistently identify predictive features across various mechanisms.
In this paper, we introduce Adaptive Cluster Lasso(ACL) method for variable selection in high dimensional sparse regression models with strongly correlated variables. To handle correlated variables, the concept of clustering or grouping variables and then pursuing model fitting is widely accepted. When the dimension is…
This paper addresses the problem of neighborhood selection for Gaussian graphical models. We present two heuristic algorithms: a forward-backward greedy algorithm for general Gaussian graphical models based on mutual information test, and a threshold-based algorithm for walk summable Gaussian graphical models. Both alg…
On the Distribution of Penalized Maximum Likelihood Estimators: The LASSO, SCAD, and Thresholdingmath.ST
We study the distributions of the LASSO, SCAD, and thresholding estimators, in finite samples and in the large-sample limit. The asymptotic distributions are derived for both the case where the estimators are tuned to perform consistent model selection and for the case where the estimators are tuned to perform conserva…