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.
Study evaluates thresholds for removing noise from DNN weights using random matrix theory.
Developed a new thresholding method that connects soft and hard thresholding.
We investigate the impact of available information on the estimation of the default probability within a generalized structural model for credit risk. The traditional structural model where default is triggered when the value of the firm's asset falls below a constant threshold is extended by relaxing the assumption of…
In this paper, we propose a new threshold-kernel jump-detection method for jump-diffusion processes, which iteratively applies thresholding and kernel methods in an approximately optimal way to achieve improved finite-sample performance. We use the expected number of jump misclassifications as the objective function to…
A new algorithm improves sample complexity for thresholding in Monte Carlo Tree Search.
We discuss the turnpike property for optimal investment and consumption problems. We find there exists a threshold value that determines the turnpike property for investment policy. The threshold value only depends on the Sharpe ratio, the riskless interest rate and the discount rate. We show that if utilities behave a…
The paper finds optimal threshold strategies for insurance companies with a positive terminal value at creeping ruin.
The paper considers an investment timing problem appearing in real options theory. Present values from an investment project are modeled by general diffusion process. We prove necessary and sufficient conditions under which an optimal investment time is induced by threshold strategy. We study also the conditions of opt…
We win EVA2025 by estimating extreme precipitation events using Peaks Over Thresholds and martingale testing.
Development of stock networks is an important approach to explore the relationship between different stocks in the era of big-data. Although a number of methods have been designed to construct the stock correlation networks, it is still a challenge to balance the selection of prominent correlations and connectivity of …
The paper discusses thresholds and bounds for accuracy in binary classification systems.
Although the threshold network is one of the most used tools to characterize the underlying structure of a stock market, the identification of the optimal threshold to construct a reliable stock network remains challenging. In this paper, the concept of dynamic consistence between the threshold network and the stock ma…
MTSSL optimizes threshold τ for better semi-supervised learning performance.
Recently, a novel family of biologically plausible online algorithms for reducing the dimensionality of streaming data has been derived from the similarity matching principle. In these algorithms, the number of output dimensions can be determined adaptively by thresholding the singular values of the input data matrix. …
In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named Sparse Tensor Alternating Thresholding for Singular Value Decomposition (STAT-SVD) is proposed. The proposed procedure featu…
Study analyzes FIT schemes under market and regulatory uncertainty.
Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.
Method selects the best deep learner for time-series prediction using Bayesian networks.
Optimal thresholds ensure curves remain embedded in flows.
Improved estimation of hedge fund tail risks using a novel model.
We consider the problem of the optimal trading strategy in the presence of linear costs, and with a strict cap on the allowed position in the market. Using Bellman's backward recursion method, we show that the optimal strategy is to switch between the maximum allowed long position and the maximum allowed short position…
Inference over tails is usually performed by fitting an appropriate limiting distribution over observations that exceed a fixed threshold. However, the choice of such threshold is critical and can affect the inferential results. Extreme value mixture models have been defined to estimate the threshold using the full dat…
Optimal rank-adaptive matrix estimation from linear measurements.
Improved AI lung ultrasound segmentation using expert confidence values.
Comparison of decision curve analysis and cost curves for model evaluation.
Sparse reconstruction approaches using the re-weighted l1-penalty have been shown, both empirically and theoretically, to provide a significant improvement in recovering sparse signals in comparison to the l1-relaxation. However, numerical optimization of such penalties involves solving problems with l1-norms in the ob…
Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) scores as anomalies. This presents a practical limitation on the applicabili…
New algorithms estimate function levels with near-optimal efficiency.
We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence. We do not ass…
In this paper, we present theorems specifying the critical values for series associated with debts arranged in the order of their duration.
Study optimal stopping times under regime-switching models with constraints.
Improves interpretability of anomaly scores in GBRBM-based detection.
We study the pricing of credit derivatives with asymmetric information. The managers have complete information on the value process of the firm and on the default threshold, while the investors on the market have only partial observations, especially about the default threshold. Different information structures are dis…
Paper improves risk estimation for rare events in sequential decisions.
Bayesian framework forecasts financial tail risks using realized volatility and nonlinear thresholds.
The paper provides high-probability bounds on false discovery proportions in conformal inference.
DeepTopPush improves accuracy at the top for complex classification tasks.
New method corrects bias in CVaR estimation for extreme risks.
We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter . Suppose the co- variance graph formed by thresholding the entries of the sample covariance matrix at is decomposed into connected components. We show that the vertex-partition induced by the thresh…
The paper examines smoothness of value function in consumption-investment models with borrowing constraints.
We investigate the probability distributions of the recurrence intervals between consecutive 1-min returns above a positive threshold or below a negative threshold of two indices and 20 individual stocks in China's stock market. The distributions of recurrence intervals for positive and negative thresho…
We study the relaxation dynamics of a financial market just after the occurrence of a crash by investigating the number of times the absolute value of an index return is exceeding a given threshold value. We show that the empirical observation of a power law evolution of the number of events exceeding the selected thre…
Paper studies community detection in censored hypergraphs using information theory.
A fast method estimates Gaussian mixture components without iterative fitting.
Quantile gradient boosted trees outperform other models in predicting NO2 concentration distributions.
SyncRank recovers global ranking from noisy comparisons with theoretical guarantees.
Develops a statistical test for IV, improving feature selection reliability.
Unified formula for training dynamics of linear networks combining lazy and balanced regimes.