link 📈🤖 Adaptive Block-Based Change-Point Detection for Sparse Spatially Clustered Data with Applications in Remote Sensing Imaging (Moore, Chu, Zhu) We present a non-parametric change-point detection approach to detect potentially sparse changes in a time series of high-dimensional observations

link 📈🤖 Scalable Variational Causal Discovery Unconstrained by Acyclicity () arXiv:2407.04992v1 Announce Type: cross Abstract: Bayesian causal discovery offers the power to quantify epistemic uncertainties among a broad range of structurally diverse causal theories potentially explaining the dat

link 📈🤖 A Causal Inference Framework for Data Rich Environments (Abadie, Agarwal, Shah) We propose a formal model for counterfactual estimation with unobserved confounding in "data-rich" settings, i.e., where there are a large number of units and a large number of measurements per unit. Our model

link 📈🤖 An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of tailoring variables () arXiv:2405.08180v1 Announce Type: new Abstract: Precision medicine stands as a transformative approach in healthcare, offering tailored treatments that can en

link 📈🤖 Robust High-Dimensional Time-Varying Coefficient Estimation (Shin, Kim) In this paper, we develop a novel high-dimensional coefficient estimation procedure based on high-frequency data. Unlike usual high-dimensional regression procedures such as LASSO, we additionally handle the heavy-tai

link 📈🤖 A Misuse of Specification Tests (Sueishi) Empirical researchers often perform model specification tests, such as Hausman tests and overidentifying restrictions tests, to assess the validity of estimators rather than that of models. This paper examines the effectiveness of such specificati

link 📈🤖 Factor and Idiosyncratic VAR Volatility Matrix Models for Heavy-Tailed High-Frequency Financial Observations (Shin, Kim, Wang et al) This paper introduces a novel process for both factor and idiosyncratic volatility matrices whose eigenvalues follow the vector auto-regressive (VAR) model.

link 📈🤖 Monitoring Violations of Differential Privacy over Time (Askin, Kutta, Dette) Auditing differential privacy has emerged as an important area of research that supports the design of privacy-preserving mechanisms. Privacy audits help to obtain empirical estimates of the privacy parameter, t

link 📈🤖 Roughness Analysis of Realized Volatility and VIX through Randomized Kolmogorov-Smirnov Distribution (Bianchi, Angelini) We introduce a novel distribution-based estimator for the Hurst parameter of log-volatility, leveraging the Kolmogorov-Smirnov statistic to assess the scaling behavior

link 📈🤖 Diffusion and Flow-based Copulas: Forgetting and Remembering Dependencies (Huk, Damoulas) Copulas are a fundamental tool for modelling multivariate dependencies in data, forming the method of choice in diverse fields and applications. However, the adoption of existing models for multimoda

link 📈🤖 Causal Machine Learning for Surgical Interventions (Tamo, Chouhan, Nnamdi et al) Surgical decision-making is complex and requires understanding causal relationships between patient characteristics, interventions, and outcomes. In high-stakes settings like spinal fusion or scoliosis correc

link 📈🤖 Testing the Constancy of Type Ia Supernova Luminosities with Gaussian Process (Rana) Type Ia supernovae (SNe~Ia) are central to studies of cosmic expansion, under the assumption that their absolute magnitude $M_B$ does not evolve with redshift. Even small drifts in brightness can bias cos

link 📈🤖 How Model Size, Temperature, and Prompt Style Affect LLM-Human Assessment Score Alignment (Jung, Lu, Benker et al) We examined how model size, temperature, and prompt style affect Large Language Models' (LLMs) alignment within itself, between models, and with human in assessing clinical r

link 📈🤖 Statistical Inference Leveraging Synthetic Data with Distribution-Free Guarantees (Bashari, Lee, Lotan et al) The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and chal

link 📈🤖 Scale two-sample testing with arbitrarily missing data (Zeng, Adams, Bodenham) This work proposes a novel rank-based scale two-sample testing method for univariate, distinct data when a subset of the data may be missing. Our approach is based on mathematically tight bounds of the Ansari-B

link 📈🤖 Transfer Learning in Regression with Influential Points (Wang, Wang, Tang) Regression prediction plays a crucial role in practical applications and strongly relies on data annotation. However, due to prohibitive annotation costs or domain-specific constraints, labeled data in the target d

link 📈🤖 Non-overlap Average Treatment Effect Bounds (Susmann, McClean, D\'iaz) The average treatment effect (ATE), the mean difference in potential outcomes under treatment and control, is a canonical causal effect. Overlap, which says that all subjects have non-zero probability of either treatme

link 📈🤖 Identification and Semiparametric Estimation of Conditional Means from Aggregate Data (McCartan, Kuriwaki) We introduce a new method for estimating the mean of an outcome variable within groups when researchers only observe the average of the outcome and group indicators across a set of a

link 📈🤖 A decision-theoretic framework for uncertainty quantification in epidemiological modelling (Steyn, Smith, Mills et al) Estimating, understanding, and communicating uncertainty is fundamental to statistical epidemiology, where model-based estimates regularly inform real-world decisions. Ho

link 📈🤖 Multi-state Models For Modeling Disease Histories Based On Longitudinal Data (Wiegrebe, Piller, Gorski et al) Multi-stage disease histories derived from longitudinal data are becoming increasingly available as registry data and biobanks expand. Multi-state models are suitable to investiga

link 📈🤖 Identification and Estimation of Seller Risk Aversion in Ascending Auctions (Gimenes, Qi, Srisuma) How sellers choose reserve prices is central to auction theory, and the optimal reserve price depends on the seller's risk attitude. Numerous studies have found that observed reserve prices

link 📈🤖 Extending finite mixture models with skew-normal distributions and hidden Markov models for time series (Nigri, Forti, Shang) We introduce an extension of finite mixture models by incorporating skew-normal distributions within a Hidden Markov Model framework. By assuming a constant transi

link 📈🤖 Decomposing Co-Movements in Matrix-Valued Time Series: A Pseudo-Structural Reduced-Rank Approach (Hecq, Ricardo, Wilms) We propose a pseudo-structural framework for analyzing contemporaneous co-movements in reduced-rank matrix autoregressive (RRMAR) models. Unlike conventional vector-auto

link 📈🤖 Improving Disease Risk Estimation in Small Areas by Accounting for Spatiotemporal Local Discontinuities (Santaf\'e, G., Adin et al) This work proposes a two-step method to enhance disease risk estimation in small areas by integrating spatiotemporal cluster detection within a Bayesian hier

link 📈🤖 Causal Inference under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatment Effects (Kubota, Sugasawa) Many marketing applications, including credit card incentive programs, offer rewards to customers who exceed specific spending thresholds to encourage increased

link 📈🤖 Generalized Bayesian Inference for Dynamic Random Dot Product Graphs (Loyal) The random dot product graph is a popular model for network data with extensions that accommodate dynamic (time-varying) networks. However, two significant deficiencies exist in the dynamic random dot product gra

link 📈🤖 Hierarchical Bayesian Operator-induced Symbolic Regression Trees for Structural Learning of Scientific Expressions (Roy, Dey, Pati et al) The advent of Scientific Machine Learning has heralded a transformative era in scientific discovery, driving progress across diverse domains. Central t

link 📈🤖 Chiseling: Powerful and Valid Subgroup Selection via Interactive Machine Learning (Cheng, Spector, Janson) In regression and causal inference, controlled subgroup selection aims to identify, with inferential guarantees, a subgroup (defined as a subset of the covariate space) on which the

link 📈🤖 Some Simplifications for the Expectation-Maximization (EM) Algorithm: The Linear Regression Model Case (Griffith) The EM algorithm is a generic tool that offers maximum likelihood solutions when datasets are incomplete with data values missing at random or completely at random. At least f

link 📈🤖 Forecasting High Dimensional Time Series with Dynamic Dimension Reduction (Pe\~na, Yohai) Many dimension reduction techniques have been developed for independent data, and most have also been extended to time series. However, these methods often fail to account for the dynamic dependencie

link 📈🤖 Particle Filtering for Non-Deterministic Electrocardiographic Imaging ((UB), Montella) Electrocardiographic imaging (ECGI) aims to non-invasively reconstruct activation maps of the heart from temporal body surface potentials. While most existing approaches rely on inverse and optimization

link 📈🤖 Accounting statement analysis at industry level () Compositional data are contemporarily defined as positive vectors, the ratios among whose elements are of interest to the researcher. Financial statement analysis by means of accounting ratios fulfils this definition to the letter. Compos

link 📈🤖 Nonparametric Bayesian analysis for the Galton-Watson process (Cannas, Guindani, Piras) The Galton-Watson process is a model for population growth which assumes that individuals reproduce independently according to the same offspring distribution. Inference usually focuses on the offsprin

link 📈🤖 Measurement-to-Track Association and Finite-Set Statistics () arXiv:1701.07078v2 Announce Type: replace Abstract: This is a shortened, clarified, and mathematically more rigorous version of the original arXiv version. Its first four findings remain unchanged from the original: 1) measure

link 📈🤖 Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data (\'Alvarez-L\'opez, Matabuena) Modeling the continuous--time dynamics of probability distributions from time--dependent data samples is a fundamental problem i

link 📈🤖 Markov Combinations of Discrete Statistical Models (Marigliano, Riccomagno) Markov combination is an operation that takes two statistical models and produces a third whose marginal distributions include those of the original models. Building upon and extending existing work in the Gaussia

link 📈🤖 Filtering amplitude dependence of correlation dynamics in complex systems: application to the cryptocurrency market (W\k{a}torek, Bezbradica, Crane et al) Based on the cryptocurrency market dynamics, this study presents a general methodology for analyzing evolving correlation structures i

link 📈🤖 Evaluating Bias Reduction Methods in Binary Emax Model for Reliable Dose-Response Estimation (Zhang, Pradhan, Zhao) The Binary Emax model is widely employed in dose-response analysis during Phase II clinical studies to identify the optimal dose for subsequence confirmatory trials. The par

link 📈🤖 On Multi-entity, Multivariate Quickest Change Point Detection (Kor, Gaikwad, Patra et al) We propose a framework for online Change Point Detection (CPD) from multi-entity, multivariate time series data, motivated by applications in crowd monitoring where traditional sensing methods (e.g.,

link 📈🤖 A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC (Llorente, Martino, Read et al) This survey gives an overview of Monte Carlo methodologies using surrogate models, for dealing with densities which are intractable, costly, an

link 📈🤖 Modified Lepage-type test statistics for the weak null hypothesis (Hussain, Tsagris) Detecting simultaneous shifts in the location and scale of two populations is a challenging problem in statistical research. A common way to address this issue is by combining location and scale test stat

link 📈🤖 Nonparametric efficient estimation of the longitudinal front-door functional (Breum, Rytgaard, Martinussen et al) The front-door criterion is an identification strategy for the intervention-specific mean outcome in settings where the standard back-door criterion fails due to unmeasured ex

link 📈🤖 Causal tail coefficient for compound extremes in multivariate time series (Yin, Sykulski, Veraart) Extreme events are often multivariate in nature. A compound extreme occurs when a combination of variables jointly produces a significant impact, even if individual components are not necess

link 📈🤖 Driver Identification and PCA Augmented Selection Shrinkage Framework for Nordic System Price Forecasting (Sadabad, Hesamzadeh, Dan et al) The System Price (SP) of the Nordic electricity market serves as a key reference for financial hedge contracts such as Electricity Price Area Differen

link 📈🤖 Dynamic Prediction in Mixture Cure Models: A Model-Based Landmarking Approach (Cipriani, Alf\`o, Signorelli) Mixture cure models are widely used in survival analysis when a portion of patients is considered cured and is no longer at risk for the event of interest. In clinical settings, dy

link 📈🤖 Optimal estimation for regression discontinuity design with binary outcomes (Ishihara, Sawada, Yata) We develop a finite-sample optimal estimator for regression discontinuity designs when the outcomes are bounded, including binary outcomes as the leading case. Our finite-sample optimal es

link 📈🤖 Tractable Approximation of Labeled Multi-Object Posterior Densities (Nguyen, Vo, Vo) Multi-object estimation in state-space models (SSMs) wherein the system state is represented as a finite set has attracted significant interest in recent years. In Bayesian inference, the posterior densit

link 📈🤖 Optimization-centric cutting feedback for semiparametric models (Tan, Nott, Frazier) Modern statistics deals with complex models from which the joint model used for inference is built by coupling submodels, called modules. We consider modular inference where the modules may depend on para

link 📈🤖 Adaptive Bayesian Joint Latent Space Modeling via Cumulative Shrinkage (Lv, Tang, Zhang) Network models are increasingly vital in psychometrics for analyzing relational data, which are often accompanied by high-dimensional node attributes. Joint latent space models (JLSM) provide an elega

link 📈🤖 Enhanced Survival Trees (Zhou, Xie, Liu et al) We introduce a new survival tree method for censored failure time data that incorporates three key advancements over traditional approaches. First, we develop a more computationally efficient splitting procedure that effectively mitigates the