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.
New algorithm learns Markov network structures efficiently.
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…
Paper detects changes in graph-based data streams using likelihood-ratios.
Develops neural network methods for likelihood ratio estimation.
In high-dimensional data analysis, penalized likelihood estimators are shown to provide superior results in both variable selection and parameter estimation. A new algorithm, APPLE, is proposed for calculating the Approximate Path for Penalized Likelihood Estimators. Both the convex penalty (such as LASSO) and the nonc…
Recently, there has been focus on penalized log-likelihood covariance estimation for sparse inverse covariance (precision) matrices. The penalty is responsible for inducing sparsity, and a very common choice is the convex norm. However, the best estimator performance is not always achieved with this penalty. The …
This article studies local and global inference for smoothing spline estimation in a unified asymptotic framework. We first introduce a new technical tool called functional Bahadur representation, which significantly generalizes the traditional Bahadur representation in parametric models, that is, Bahadur [Ann. Inst. S…
New method for robust distribution alignment using log-likelihood ratio and normalizing flows.
This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.
Researchers develop a method to learn CTBN structure using penalized maximum likelihood.
Neural networks approximate likelihood ratios for complex models.
New model handles complex non-linear relationships with hidden graph structures.
FF algorithm uses goodness as a likelihood-ratio test for scalar normalization.
In many applications, data come with a natural ordering. This ordering can often induce local dependence among nearby variables. However, in complex data, the width of this dependence may vary, making simple assumptions such as a constant neighborhood size unrealistic. We propose a framework for learning this local dep…
We develop a maximum penalized quasi-likelihood estimator for estimating in a nonparametric way the diffusion function of a diffusion process, as an alternative to more traditional kernel-based estimators. After developing a numerical scheme for computing the maximizer of the penalized maximum quasi-likelihood function…
Direct neural ratio estimator for likelihood-free inference.
FF algorithm uses goodness as a measure of input quality, derived from likelihood-ratio tests.
Gen-LRA attacks synthetic data leakage without model knowledge.
Unified analysis of multi-attribute graph learning with non-convex penalties.
Improved convergence rates for MLE in mixture models using penalized log-likelihood.
Novel neural likelihood ratio estimation for negative data in particle physics.
We consider the problem of sparse estimation in a factor analysis model. A traditional estimation procedure in use is the following two-step approach: the model is estimated by maximum likelihood method and then a rotation technique is utilized to find sparse factor loadings. However, the maximum likelihood estimates c…
Paper explores supervised learning methods to approximate ideal observer for joint signal detection and localization.
To better understand the interplay of censoring and sparsity we develop finite sample properties of nonparametric Cox proportional hazard's model. Due to high impact of sequencing data, carrying genetic information of each individual, we work with over-parametrized problem and propose general class of group penalties s…
In this paper we present nonparametric estimators for coefficients in stochastic differential equation if the data are described by independent, identically distributed random variables. The problem is formulated as a nonlinear ill-posed operator equation with a deterministic forward operator described by the Fokker-Pl…
We introduce a dynamic credit portfolio framework where optimal investment strategies are robust against misspecifications of the reference credit model. The risk-averse investor models his fear of credit risk misspecification by considering a set of plausible alternatives whose expected log likelihood ratios are penal…
The paper proposes an asset allocation strategy using the Sortino ratio for better performance.
Study EM and GD for clustering with penalties for misspecification and high dimensions.
Researchers develop methods for inference in hierarchical models using neural simulations.
Sparse multinomial logistic regression for multiclass classification with feature selection.
It has been shown that AIC-type criteria are asymptotically efficient selectors of the tuning parameter in non-concave penalized regression methods under the assumption that the population variance is known or that a consistent estimator is available. We relax this assumption to prove that AIC itself is asymptotically …
New machine learning methods for inference from simulated data.
This paper tackles model selection for MoE models in high-dimensional data.
Markov regime switching models have been used in numerous empirical studies in economics and finance. However, the asymptotic distribution of the likelihood ratio test statistic for testing the number of regimes in Markov regime switching models has been an unresolved problem. This paper derives the asymptotic distribu…
In this note, we study the relationship between the variational gap and the variance of the (log) likelihood ratio. We show that the gap can be upper bounded by some form of dispersion measure of the likelihood ratio, which suggests the bias of variational inference can be reduced by making the distribution of the like…
In a Gaussian graphical model, the conditional independence between two variables are characterized by the corresponding zero entries in the inverse covariance matrix. Maximum likelihood method using the smoothly clipped absolute deviation (SCAD) penalty (Fan and Li, 2001) and the adaptive LASSO penalty (Zou, 2006) hav…
The paper improves Lasso inference methods for survey data.
Proposes GAGA algorithm for automatic hyperparameter learning in signal recovery.
The paper proposes a method to construct confidence sets using likelihood ratios for sequential decision-making.
Study detects signals in spiked Wigner models using log likelihood ratio.
Optimal selective classification using likelihood ratios improves model reliability.
ANODE uses neural density estimation for anomaly detection in physics.
In many fields of science, generalized likelihood ratio tests are established tools for statistical inference. At the same time, it has become increasingly common that a simulator (or generative model) is used to describe complex processes that tie parameters of an underlying theory and measurement apparatus to hig…
Posterior inference with an intractable likelihood is becoming an increasingly common task in scientific domains which rely on sophisticated computer simulations. Typically, these forward models do not admit tractable densities forcing practitioners to make use of approximations. This work introduces a novel approach t…
Graphical Gaussian models have proven to be useful tools for exploring network structures based on multivariate data. Applications to studies of gene expression have generated substantial interest in these models, and resulting recent progress includes the development of fitting methodology involving penalization of th…
Introduces BPEL for EL, enhancing flexibility and using MCMC for inference.
Optimally tackles covariate shift in RKHS-based nonparametric regression.
We introduce a Cox-type model for relative intensities of orders flows in a limit order book. The model assumes that all intensities share a common baseline intensity, which may for example represent the global market activity. Parameters can be estimated by quasi likelihood maximization, without any interference from …