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ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 50m

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An utopic adventure in the modelling of conditional univariate and multivariate extremes () The EVA 2023 data competition consisted of four challenges, ranging from
interval estimation for very high quantiles of univariate extremes conditional
on covariates, point estimation of unconditio

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 3h

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Matern Correlation: A Panoramic Primer () arXiv:2404.11427v1 Announce Type: new
Abstract: Matern correlation is of pivotal importance in spatial statistics and machine learning. This paper serves as a panoramic primer for this correlation with an emphasis on the exposition of its changin

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Tensor time series change-point detection in cryptocurrency network data (Anastasiou, Cribben) Financial fraud has been growing exponentially in recent years. The rise of cryptocurrencies as an investment asset has simultaneously seen a parallel growth in cryptocurrency scams. To detect p

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Power-divergence copulas: A new class of Archimedean copulas, with an insurance application (Pearse, Bondell) This paper demonstrates that, under a particular convention, the convex functions that characterise the phi divergences also generate Archimedean copulas in at least two dimension

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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A GNAR-Based Framework for Spectral Estimation of Network Time Series: Application to Global Bank Network Connectedness (Jim\'enez-Var\'on, Knight) Patterns of dependence in financial networks, such as global bank connectedness, evolve over time and across frequencies. Analysing these sys

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Geometric Model Selection for Latent Space Network Models: Hypothesis Testing via Multidimensional Scaling and Resampling Techniques (Wang, Smith) Latent space models assume that network ties are more likely between nodes that are closer together in an underlying latent space. Euclidean s

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Automated Gating for Flow Cytometry Data Using a Kernel-Smoothed EM Algorithm (Sousa, Ribalet, Bien) Phytoplankton are microscopic algae responsible for roughly half of the world's photosynthesis that play a critical role in global carbon cycles and oxygen production, and measuring the ab

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Robust Inference for Convex Pairwise Difference Estimators (Cattaneo, Jansson, Nagasawa) This paper develops distribution theory and bootstrap-based inference methods for a broad class of convex pairwise difference estimators. These estimators minimize a kernel-weighted convex-in-paramete

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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A subsampling approach for large data sets when the Generalised Linear Model is potentially misspecified (Mahendran, Thompson, McGree) Subsampling is a computationally efficient and scalable method to draw inference in large data settings based on a subset of the data rather than needing

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Extension of Wald-Wolfowitz Runs Test for Regression Validity Testing with Repeated Measures of Independent Variable (Lian, Chen) The Wald-Wolfowitz runs test can assess the correctness of a regression curve fitted to a data set with one independent parameter. The assessment is performed

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ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Assessing the Effects of Monetary Shocks on Macroeconomic Stars: A SMUC-IV Framework (Fu, Hou, Pr\"user) This paper proposes a structural multivariate unobserved components model with external instrument (SMUC-IV) to investigate the effects of monetary policy shocks on key U.S. macroecono

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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A Bivariate DAR($1$) model for ordinal time series (Nalpantidi, Karlis) We present a bivariate vector valued discrete autoregressive model of order $1$ (BDAR($1$)) for discrete time series. The BDAR($1$) model assumes that each time series follows its own univariate DAR($1$) model with de

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Correcting sample selection bias with categorical outcomes (Boussim) In this paper, we propose a method for correcting sample selection bias when the outcome of interest is categorical, such as occupational choice, health status, or field of study. Classical approaches to sample selection

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Can language models boost the power of randomized experiments without statistical bias? (Ruan, Ma, Wang et al) Randomized experiments or randomized controlled trials (RCTs) are gold standards for causal inference, yet cost and sample-size constraints limit power. Meanwhile, modern RCTs ro

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Estimating Treatment Effects Under Bounded Heterogeneity (Kwon, Sun) Researchers often use specifications that correctly estimate the average treatment effect under the assumption of constant effects. When treatment effects are heterogeneous, however, such specifications generally fail to

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Sparse-Group Factor Analysis for High-Dimensional Time Series (Wang, Liu) Factor analysis is a widely used technique for dimension reduction in high-dimensional data. However, a key challenge in factor models lies in the interpretability of the latent factors. One intuitive way to interpr

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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A new composite Mann-Whitney test for two-sample survival comparisons with right-censored data (Hussain, Ahmad) A fundamental challenge in comparing two survival distributions with right censored data is the selection of an appropriate nonparametric test, as the power of standard tests li

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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An efficient hybrid approach of quantile and expectile regression (Atanane, Mkhadri, Oualkacha) Quantiles and expectiles are determined by different loss functions: asymmetric least absolute deviation for quantiles and asymmetric squared loss for expectiles. This distinction ensures that

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 6h

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Procrustes Problems on Random Matrices (Jasa, Bergmann, K\"ummerle et al) Meaningful comparison between sets of observations often necessitates alignment or registration between them, and the resulting optimization problems range in complexity from those admitting simple closed-form solut

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 19h

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Privacy-Preserving Community Detection for Locally Distributed Multiple Networks () arXiv:2306.15709v2 Announce Type: replace-cross
Abstract: Modern multi-layer networks are commonly stored and analyzed in a local and distributed fashion because of the privacy, ownership, and communicati

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 21h

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A Nonparametric Bayes Approach to Online Activity Prediction () Accurately predicting the onset of specific activities within defined
timeframes holds significant importance in several applied contexts. In
particular, accurate prediction of the number of future users that will be
exposed

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 1d

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An IPCW Adjusted Win Statistics Approach in Clinical Trials Incorporating Equivalence Margins to Define Ties (Cui, Huang, Dong et al) In clinical trials, multiple outcomes of different priorities commonly occur as the patient's response may not be adequately characterized by a single outc

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 1d

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iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models () Structural causal models (SCMs) are widely used in various disciplines to
represent causal relationships among variables in complex systems.
Unfortunately, the underlying causal structure is often unknown,

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 1d

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KL-BSS: Rethinking optimality for neighbourhood selection in structural equation models (Gao, Tai, Aragam) We introduce a new method for neighbourhood selection in linear structural equation models that improves over classical methods such as best subset selection (BSS) and the Lasso. Our

ArXiv Paperboy (Stat.ME+Econ.EM) @paperposterbot.bsky.social · 1d

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Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models (M\"osching, Li, Munk) Hidden Markov models (HMMs) are characterized by an unobservable Markov chain and an observable process -- a noisy version of the hidden chain. Decoding the original signal