arXiv preprints from January 1, 2026 through September 23, 2026 — 11:32:55 EST
It is obvious to say that an adequate estimation of the autocorrelation function is central in time series analysis. In this paper, we propose three new robust estimators based on ratios of observations, which offer strong resistance against outliers. While the first estimator, which is based on the median, is not efficient, the...
Firms struggle to choose AI projects that pay off: two projects can look equally promising to smart, motivated stakeholders and yet deserve opposite decisions. At the residential real-estate brokerage Compass, one AI product (Likely-to-Sell recommendations) flagged sales outreach opportunities and went on to account for nine figures...
I critique a set of entrenched methodological conventions that collectively create systemic dysfunction in statistical research for clinical decisions. These include: (1) the prevalent use of hypothesis tests to compare treatments, (2) remoteness from patient care of the methods used to evaluate the accuracy of predictions of patient...
Let $V$ and $U$ be independent standard normal random variables. For any Borel map $φ\colon\mathbb{R}\to\mathbb{R}$, set $Y_φ=φ(V)+U$, and define $P_φ(y)=\mathbb{E}[V\mid Y_φ=y]$ and $F_φ(x)=\mathbb{E}[P_φ(x+U)]$. We prove that, if for every $v\in\mathbb{R}$, the quantity $φ(v)$ maximises $x(v-F_φ(x))$ over $x\in\mathbb{R}$, then $φ$...
Firms covered by emissions trading systems need forecasts not only to value allowances, but also to decide when to buy them. This paper asks whether European Union Allowance (EUA) prices contain short-horizon predictability that survives a forecast-origin information design and improves simulated compliance procurement. Using daily...
A red herring, an irrelevant passage added to a problem, makes a language model reason incorrectly and answer incorrectly far more often. Yet the model still writes out a full explanation, and the answer it gives remains consistent with the steps it shows. The red herring corrupts the reasoning, while leaving the explanation intact...
Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance. Bitcoin exhibits fat-tailed returns, non-stationary dynamics, and a price discovery process influenced by social discourse on Reddit and Twitter. Conventional approaches fuse OHLCV technical features with sentiment via static...
We study whether strictly positive marginal values restore the compatibility of envy-freeness up to one good (EF1) and Pareto optimality (PO) for indivisible goods. For two agents, we identify the exact threshold in the number of goods. Every instance with at most seven goods and strictly increasing valuations admits an allocation...
When does the global game information structure select a unique equilibrium? Limit uniqueness in two-player supermodular games fails exactly when a risk-dominant better response cycle exists (Veiel, 2025). We show that rank-one factor structure on payoffs eliminates such cycles entirely, so every rank-one supermodular game admits a...
Age is a match trait and a prominent label on search platforms. Using confidential records from a large Japanese marriage platform, I study how a birthday age update affects consideration, applications, relationship progression, and engagement. Only displayed age updates at birthdays. Receivers enter some acceptable-age ranges as they...
Rigorous economic models can take months to construct, yet energy crises demand decisions from policymakers within days or even hours. Any disruption in energy markets is not isolated but rapidly disseminates through interlinked global systems. Off-the-shelf models that already exist typically focus only on limited aspects of the...
Understanding differences in microbial community structure is critical for uncovering risk factors and mechanisms underlying diseases such as colorectal cancer and preterm birth. Microbiome data present unique statistical challenges because they are compositional in nature, violating assumptions of many classical inference procedures....
Deep generative models (DGMs) are widely used for complex high-dimensional data and increasingly applied to spatial and spatio-temporal modeling. Their generated samples implicitly represent the learned data distribution and associated uncertainty. However, for real-world data, assessing whether DGMs have learned the underlying...
We illustrate in several examples that even neural networks of infinite width (specifically, Barron functions) may encounter substantial obstacles when used as a model class for problems in the calculus of variations. An instance of practical relevance concerns the bending, stretching and folding of a thin elastic shell with anchored...
Large-scale observational studies increasingly rely on AI pipelines to extract structured variables from unstructured clinical records. A common workflow separates the data vendor, who validates extraction accuracy with a gold-standard sample, from the downstream researcher, who receives only the extracted dataset and summary accuracy...
Wind-power variability is a major challenge for the reliable integration of utility-scale wind energy into modern power systems. Although wind-speed statistics are often described by simple parametric distributions, translating these statistics into turbine-level power fluctuations is nontrivial because the relationship between wind...
Meta-analyzing natural indirect effect estimates from multiple studies is increas- ingly used to synthesize evidence on causal pathways of interest. However, stan- dard mediation meta-analysis approaches are typically based on structural equation modeling, which fails to account for mediator-outcome confounding, is not read- ily...
Two-study meta-analyses are common in evidence synthesis but pose major statistical challenges. With only two studies, the between-study variance cannot be reliably estimated, rendering standard random-effects methods unstable. Here, we investigate meta-analyses based on Edgington's p-value combination method as an alternative...
The Expected Value of Sample Information (EVSI) is a powerful instrument to determine the value of additional evidence to inform an economic model. However, EVSI has been applied only to idealized data collection mechanisms, thereby reducing its potential applications in realistic studies. In this paper, we define a methodology to...
Many statistical problems concern the analysis of species distributions or, more generally, of discrete labeled quantities. Assessing species diversity constitutes a key step toward understanding population structure, and the Gini-Simpson index is among the most widely adopted diversity measures. In this manuscript, we examine several...
The framework of recanting twin path-specific effects has recently been propose to address the issue of intermediate confounding in causal mediation analysis, enabling the decomposition of the average treatment effect into identifiable fine-grained path-specific effects (PSEs). An open question, however, is the extent to which...
Generalised Bayesian inference (GBI) has emerged as a compelling robust alternative to standard Bayesian inference, mitigating sensitivity to data contamination by replacing the log-likelihood with a robust loss or divergence. However, existing robust GBI frameworks typically provide only qualitative robustness: while they can make...
Machine learning demand forecasts optimize statistical accuracy yet leave excess operational volatility that inflates safety stock and amplifies the Bullwhip effect. We introduce \textbf{Contextual Deconvolution} (CD), a two-stage estimator that reframes demand sensing as a convex decomposition: a kernel-modulated banded operator...
Among chess opening positions that a strong engine judges essentially equal (Stockfish 18 evaluation within 10 centipawns of zero, depth-stable) and that humans actually reach on Lichess (October 2025; 1,661 positions, 16.1M occurrences), human results are not balanced. Positions carry outcome skews, each the gap between its games'...
The collective risk model is a fundamental framework in insurance ratemaking for modeling aggregate losses by combining claim frequency and claim severity components. A key structural requirement for a reliable experience rating system is a monotone ordering property: policyholders with worse past experience should receive a...