ArXiv Paperboy (Stat.ME+Econ.EM)
@paperposterbot.bsky.social
posts updates from arXiv rss feeds for methodology papers in Statistics and Econometrics. Also maintains an arxiv and posts random papers from it.
maintainer: @apoorvalal.com
source code: https://github.com/apoorvalal/bsky_paperbot
maintainer: @apoorvalal.com
source code: https://github.com/apoorvalal/bsky_paperbot
link 📈🤖
A Bayesian mixture model for Poisson network autoregression () arXiv:2411.14265v1 Announce Type: new
Abstract: In this paper, we propose a new Bayesian Poisson network autoregression mixture model (PNARM). Our model combines ideas from the models of Dahl 2008, Ren et al. 2024 and Armillo
A Bayesian mixture model for Poisson network autoregression () arXiv:2411.14265v1 Announce Type: new
Abstract: In this paper, we propose a new Bayesian Poisson network autoregression mixture model (PNARM). Our model combines ideas from the models of Dahl 2008, Ren et al. 2024 and Armillo
November 26, 2025 at 10:06 PM
link 📈🤖
A Bayesian mixture model for Poisson network autoregression () arXiv:2411.14265v1 Announce Type: new
Abstract: In this paper, we propose a new Bayesian Poisson network autoregression mixture model (PNARM). Our model combines ideas from the models of Dahl 2008, Ren et al. 2024 and Armillo
A Bayesian mixture model for Poisson network autoregression () arXiv:2411.14265v1 Announce Type: new
Abstract: In this paper, we propose a new Bayesian Poisson network autoregression mixture model (PNARM). Our model combines ideas from the models of Dahl 2008, Ren et al. 2024 and Armillo
link 📈🤖
Roughness Signature Functions () Inspired by the activity signature introduced by Todorov and Tauchen (2010),
which was used to measure the activity of a semimartingale, this paper
introduces the roughness signature function. The paper illustrates how it can
be used to determine whether a
Roughness Signature Functions () Inspired by the activity signature introduced by Todorov and Tauchen (2010),
which was used to measure the activity of a semimartingale, this paper
introduces the roughness signature function. The paper illustrates how it can
be used to determine whether a
November 26, 2025 at 7:04 PM
link 📈🤖
Roughness Signature Functions () Inspired by the activity signature introduced by Todorov and Tauchen (2010),
which was used to measure the activity of a semimartingale, this paper
introduces the roughness signature function. The paper illustrates how it can
be used to determine whether a
Roughness Signature Functions () Inspired by the activity signature introduced by Todorov and Tauchen (2010),
which was used to measure the activity of a semimartingale, this paper
introduces the roughness signature function. The paper illustrates how it can
be used to determine whether a
link 📈🤖
Quantile-Frequency Analysis and Spectral Measures for Diagnostic Checks of Time Series With Nonlinear Dynamics (Li) Nonlinear dynamic volatility has been observed in many financial time series. The recently proposed quantile periodogram offers an alternative way to examine this phenomena
Quantile-Frequency Analysis and Spectral Measures for Diagnostic Checks of Time Series With Nonlinear Dynamics (Li) Nonlinear dynamic volatility has been observed in many financial time series. The recently proposed quantile periodogram offers an alternative way to examine this phenomena
November 26, 2025 at 5:14 PM
link 📈🤖
Quantile-Frequency Analysis and Spectral Measures for Diagnostic Checks of Time Series With Nonlinear Dynamics (Li) Nonlinear dynamic volatility has been observed in many financial time series. The recently proposed quantile periodogram offers an alternative way to examine this phenomena
Quantile-Frequency Analysis and Spectral Measures for Diagnostic Checks of Time Series With Nonlinear Dynamics (Li) Nonlinear dynamic volatility has been observed in many financial time series. The recently proposed quantile periodogram offers an alternative way to examine this phenomena
link 📈🤖
Multiple Randomization Designs: Estimation and Inference with Interference (Masoero, Vijaykumar, Richardson et al) In this study we introduce a new class of experimental designs. In a classical randomized controlled trial (RCT), or A/B test, a randomly selected subset of a population of u
Multiple Randomization Designs: Estimation and Inference with Interference (Masoero, Vijaykumar, Richardson et al) In this study we introduce a new class of experimental designs. In a classical randomized controlled trial (RCT), or A/B test, a randomly selected subset of a population of u
November 26, 2025 at 5:11 PM
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Multiple Randomization Designs: Estimation and Inference with Interference (Masoero, Vijaykumar, Richardson et al) In this study we introduce a new class of experimental designs. In a classical randomized controlled trial (RCT), or A/B test, a randomly selected subset of a population of u
Multiple Randomization Designs: Estimation and Inference with Interference (Masoero, Vijaykumar, Richardson et al) In this study we introduce a new class of experimental designs. In a classical randomized controlled trial (RCT), or A/B test, a randomly selected subset of a population of u
link 📈🤖
An Infinite BART model (Battiston, Luo) Bayesian additive regression trees (BART) are popular Bayesian ensemble models used in regression and classification analysis. Under this modeling framework, the regression function is approximated by an ensemble of decision trees, interpreted as we
An Infinite BART model (Battiston, Luo) Bayesian additive regression trees (BART) are popular Bayesian ensemble models used in regression and classification analysis. Under this modeling framework, the regression function is approximated by an ensemble of decision trees, interpreted as we
November 26, 2025 at 5:09 PM
link 📈🤖
An Infinite BART model (Battiston, Luo) Bayesian additive regression trees (BART) are popular Bayesian ensemble models used in regression and classification analysis. Under this modeling framework, the regression function is approximated by an ensemble of decision trees, interpreted as we
An Infinite BART model (Battiston, Luo) Bayesian additive regression trees (BART) are popular Bayesian ensemble models used in regression and classification analysis. Under this modeling framework, the regression function is approximated by an ensemble of decision trees, interpreted as we
link 📈🤖
An Efficient Adaptive Sequential Procedure for Simple Hypotheses with Expression for Finite Number of Applications of Less Effective Treatment (Kundu, Jha, Bhandari) We propose an adaptive sequential framework for testing two simple hypotheses that analytically ensures finite exposure to
An Efficient Adaptive Sequential Procedure for Simple Hypotheses with Expression for Finite Number of Applications of Less Effective Treatment (Kundu, Jha, Bhandari) We propose an adaptive sequential framework for testing two simple hypotheses that analytically ensures finite exposure to
November 26, 2025 at 5:04 PM
link 📈🤖
An Efficient Adaptive Sequential Procedure for Simple Hypotheses with Expression for Finite Number of Applications of Less Effective Treatment (Kundu, Jha, Bhandari) We propose an adaptive sequential framework for testing two simple hypotheses that analytically ensures finite exposure to
An Efficient Adaptive Sequential Procedure for Simple Hypotheses with Expression for Finite Number of Applications of Less Effective Treatment (Kundu, Jha, Bhandari) We propose an adaptive sequential framework for testing two simple hypotheses that analytically ensures finite exposure to
link 📈🤖
Sigmoid-FTRL: Design-Based Adaptive Neyman Allocation for AIPW Estimators (Chen, Ge, Qian et al) We consider the problem of Adaptive Neyman Allocation for the class of AIPW estimators in a design-based setting, where potential outcomes and covariates are deterministic. As each subject arr
Sigmoid-FTRL: Design-Based Adaptive Neyman Allocation for AIPW Estimators (Chen, Ge, Qian et al) We consider the problem of Adaptive Neyman Allocation for the class of AIPW estimators in a design-based setting, where potential outcomes and covariates are deterministic. As each subject arr
November 26, 2025 at 5:02 PM
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Sigmoid-FTRL: Design-Based Adaptive Neyman Allocation for AIPW Estimators (Chen, Ge, Qian et al) We consider the problem of Adaptive Neyman Allocation for the class of AIPW estimators in a design-based setting, where potential outcomes and covariates are deterministic. As each subject arr
Sigmoid-FTRL: Design-Based Adaptive Neyman Allocation for AIPW Estimators (Chen, Ge, Qian et al) We consider the problem of Adaptive Neyman Allocation for the class of AIPW estimators in a design-based setting, where potential outcomes and covariates are deterministic. As each subject arr
link 📈🤖
Clustering Approaches for Mixed-Type Data: A Comparative Study (Ghattas, San-Benito) Clustering is widely used in unsupervised learning to find homogeneous groups of observations within a dataset. However, clustering mixed-type data remains a challenge, as few existing approaches are suit
Clustering Approaches for Mixed-Type Data: A Comparative Study (Ghattas, San-Benito) Clustering is widely used in unsupervised learning to find homogeneous groups of observations within a dataset. However, clustering mixed-type data remains a challenge, as few existing approaches are suit
November 26, 2025 at 5:01 PM
link 📈🤖
Clustering Approaches for Mixed-Type Data: A Comparative Study (Ghattas, San-Benito) Clustering is widely used in unsupervised learning to find homogeneous groups of observations within a dataset. However, clustering mixed-type data remains a challenge, as few existing approaches are suit
Clustering Approaches for Mixed-Type Data: A Comparative Study (Ghattas, San-Benito) Clustering is widely used in unsupervised learning to find homogeneous groups of observations within a dataset. However, clustering mixed-type data remains a challenge, as few existing approaches are suit
link 📈🤖
Anchoring Convenience Survey Samples to a Baseline Census for Vaccine Coverage Monitoring in Global Health (Dyrkton, Alam, Shepherd et al) While conducting probabilistic surveys is the gold standard for assessing vaccine coverage, implementing these surveys poses challenges for global hea
Anchoring Convenience Survey Samples to a Baseline Census for Vaccine Coverage Monitoring in Global Health (Dyrkton, Alam, Shepherd et al) While conducting probabilistic surveys is the gold standard for assessing vaccine coverage, implementing these surveys poses challenges for global hea
November 26, 2025 at 4:59 PM
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Anchoring Convenience Survey Samples to a Baseline Census for Vaccine Coverage Monitoring in Global Health (Dyrkton, Alam, Shepherd et al) While conducting probabilistic surveys is the gold standard for assessing vaccine coverage, implementing these surveys poses challenges for global hea
Anchoring Convenience Survey Samples to a Baseline Census for Vaccine Coverage Monitoring in Global Health (Dyrkton, Alam, Shepherd et al) While conducting probabilistic surveys is the gold standard for assessing vaccine coverage, implementing these surveys poses challenges for global hea
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Beyond the ACE Score: Replicable Combinations of Adverse Childhood Experiences That Worsen Depression Risk (Zhang, Kong, Small et al) Adverse childhood experiences (ACEs) are categories of childhood abuse, neglect, and household dysfunction. Screening by a single additive ACE score (e.g.,
Beyond the ACE Score: Replicable Combinations of Adverse Childhood Experiences That Worsen Depression Risk (Zhang, Kong, Small et al) Adverse childhood experiences (ACEs) are categories of childhood abuse, neglect, and household dysfunction. Screening by a single additive ACE score (e.g.,
November 26, 2025 at 4:56 PM
link 📈🤖
Beyond the ACE Score: Replicable Combinations of Adverse Childhood Experiences That Worsen Depression Risk (Zhang, Kong, Small et al) Adverse childhood experiences (ACEs) are categories of childhood abuse, neglect, and household dysfunction. Screening by a single additive ACE score (e.g.,
Beyond the ACE Score: Replicable Combinations of Adverse Childhood Experiences That Worsen Depression Risk (Zhang, Kong, Small et al) Adverse childhood experiences (ACEs) are categories of childhood abuse, neglect, and household dysfunction. Screening by a single additive ACE score (e.g.,
link 📈🤖
RFX: High-Performance Random Forests with GPU Acceleration and QLORA Compression (Kuchar) RFX (Random Forests X), where X stands for compression or quantization, presents a production-ready implementation of Breiman and Cutler's Random Forest classification methodology in Python. RFX v1.0
RFX: High-Performance Random Forests with GPU Acceleration and QLORA Compression (Kuchar) RFX (Random Forests X), where X stands for compression or quantization, presents a production-ready implementation of Breiman and Cutler's Random Forest classification methodology in Python. RFX v1.0
November 26, 2025 at 4:52 PM
link 📈🤖
RFX: High-Performance Random Forests with GPU Acceleration and QLORA Compression (Kuchar) RFX (Random Forests X), where X stands for compression or quantization, presents a production-ready implementation of Breiman and Cutler's Random Forest classification methodology in Python. RFX v1.0
RFX: High-Performance Random Forests with GPU Acceleration and QLORA Compression (Kuchar) RFX (Random Forests X), where X stands for compression or quantization, presents a production-ready implementation of Breiman and Cutler's Random Forest classification methodology in Python. RFX v1.0
link 📈🤖
Big Wins, Small Net Gains: Direct and Spillover Effects of First Industry Entries in Puerto Rico (Arroyo) I study how first sizable industry entries reshape local and neighboring labor markets in Puerto Rico. Using over a decade of quarterly municipality--industry data (2014Q1--2025Q1), I
Big Wins, Small Net Gains: Direct and Spillover Effects of First Industry Entries in Puerto Rico (Arroyo) I study how first sizable industry entries reshape local and neighboring labor markets in Puerto Rico. Using over a decade of quarterly municipality--industry data (2014Q1--2025Q1), I
November 26, 2025 at 4:50 PM
link 📈🤖
Big Wins, Small Net Gains: Direct and Spillover Effects of First Industry Entries in Puerto Rico (Arroyo) I study how first sizable industry entries reshape local and neighboring labor markets in Puerto Rico. Using over a decade of quarterly municipality--industry data (2014Q1--2025Q1), I
Big Wins, Small Net Gains: Direct and Spillover Effects of First Industry Entries in Puerto Rico (Arroyo) I study how first sizable industry entries reshape local and neighboring labor markets in Puerto Rico. Using over a decade of quarterly municipality--industry data (2014Q1--2025Q1), I
link 📈🤖
Extrapolating into the Extremes with Minimum Distance Estimation (Boulin, Haufs) Understanding complex dependencies and extrapolating beyond observations are key challenges in modeling environmental space-time extremes. To address this, we introduce a simplifying approach that projects a
Extrapolating into the Extremes with Minimum Distance Estimation (Boulin, Haufs) Understanding complex dependencies and extrapolating beyond observations are key challenges in modeling environmental space-time extremes. To address this, we introduce a simplifying approach that projects a
November 26, 2025 at 4:45 PM
link 📈🤖
Extrapolating into the Extremes with Minimum Distance Estimation (Boulin, Haufs) Understanding complex dependencies and extrapolating beyond observations are key challenges in modeling environmental space-time extremes. To address this, we introduce a simplifying approach that projects a
Extrapolating into the Extremes with Minimum Distance Estimation (Boulin, Haufs) Understanding complex dependencies and extrapolating beyond observations are key challenges in modeling environmental space-time extremes. To address this, we introduce a simplifying approach that projects a
link 📈🤖
A novel multi-exposure-to-multi-mediator mediation model for imaging genetic study of brain disorders (Wang, Slud, Ma) Common psychiatric and brain disorders are highly heritable and affected by a number of genetic risk factors, yet the mechanism by which these genetic factors contribute
A novel multi-exposure-to-multi-mediator mediation model for imaging genetic study of brain disorders (Wang, Slud, Ma) Common psychiatric and brain disorders are highly heritable and affected by a number of genetic risk factors, yet the mechanism by which these genetic factors contribute
November 26, 2025 at 4:41 PM
link 📈🤖
A novel multi-exposure-to-multi-mediator mediation model for imaging genetic study of brain disorders (Wang, Slud, Ma) Common psychiatric and brain disorders are highly heritable and affected by a number of genetic risk factors, yet the mechanism by which these genetic factors contribute
A novel multi-exposure-to-multi-mediator mediation model for imaging genetic study of brain disorders (Wang, Slud, Ma) Common psychiatric and brain disorders are highly heritable and affected by a number of genetic risk factors, yet the mechanism by which these genetic factors contribute
link 📈🤖
Pseudo-strata learning via maximizing misclassification reward (Luo, Wu, Geng) Online advertising aims to increase user engagement and maximize revenue, but users respond heterogeneously to ad exposure. Some users purchase only when exposed to ads, while others purchase regardless of expo
Pseudo-strata learning via maximizing misclassification reward (Luo, Wu, Geng) Online advertising aims to increase user engagement and maximize revenue, but users respond heterogeneously to ad exposure. Some users purchase only when exposed to ads, while others purchase regardless of expo
November 26, 2025 at 4:38 PM
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Pseudo-strata learning via maximizing misclassification reward (Luo, Wu, Geng) Online advertising aims to increase user engagement and maximize revenue, but users respond heterogeneously to ad exposure. Some users purchase only when exposed to ads, while others purchase regardless of expo
Pseudo-strata learning via maximizing misclassification reward (Luo, Wu, Geng) Online advertising aims to increase user engagement and maximize revenue, but users respond heterogeneously to ad exposure. Some users purchase only when exposed to ads, while others purchase regardless of expo
link 📈🤖
Wilcoxon-Mann-Whitney Test of No Group Discrimination (Grendar) The traditional WMW null hypothesis $H_0: F = G$ is erroneously too broad. WMW actually tests narrower $H_0: AUC = 0.5$. Asymptotic distribution of the standardized $U$ statistic (i.e., the empirical AUC) under the correct $H
Wilcoxon-Mann-Whitney Test of No Group Discrimination (Grendar) The traditional WMW null hypothesis $H_0: F = G$ is erroneously too broad. WMW actually tests narrower $H_0: AUC = 0.5$. Asymptotic distribution of the standardized $U$ statistic (i.e., the empirical AUC) under the correct $H
November 26, 2025 at 4:36 PM
link 📈🤖
Wilcoxon-Mann-Whitney Test of No Group Discrimination (Grendar) The traditional WMW null hypothesis $H_0: F = G$ is erroneously too broad. WMW actually tests narrower $H_0: AUC = 0.5$. Asymptotic distribution of the standardized $U$ statistic (i.e., the empirical AUC) under the correct $H
Wilcoxon-Mann-Whitney Test of No Group Discrimination (Grendar) The traditional WMW null hypothesis $H_0: F = G$ is erroneously too broad. WMW actually tests narrower $H_0: AUC = 0.5$. Asymptotic distribution of the standardized $U$ statistic (i.e., the empirical AUC) under the correct $H
link 📈🤖
A Generalized Additive Partial-Mastery Cognitive Diagnosis Model (C\'ardenas-Hurtado, Chen, Moustaki) Cognitive diagnosis models (CDMs) are restricted latent class models widely used for measuring attributes of interest in diagnostic assessments in education, psychology, biomedical scienc
A Generalized Additive Partial-Mastery Cognitive Diagnosis Model (C\'ardenas-Hurtado, Chen, Moustaki) Cognitive diagnosis models (CDMs) are restricted latent class models widely used for measuring attributes of interest in diagnostic assessments in education, psychology, biomedical scienc
November 26, 2025 at 4:32 PM
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A Generalized Additive Partial-Mastery Cognitive Diagnosis Model (C\'ardenas-Hurtado, Chen, Moustaki) Cognitive diagnosis models (CDMs) are restricted latent class models widely used for measuring attributes of interest in diagnostic assessments in education, psychology, biomedical scienc
A Generalized Additive Partial-Mastery Cognitive Diagnosis Model (C\'ardenas-Hurtado, Chen, Moustaki) Cognitive diagnosis models (CDMs) are restricted latent class models widely used for measuring attributes of interest in diagnostic assessments in education, psychology, biomedical scienc
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Rectangular augmented row-column designs generated from contractions (Piepho, Williams) Row-column designs play an important role in applications where two orthogonal sources of error need to be controlled for by blocking. Field or greenhouse experiments, in which experimental units are a
Rectangular augmented row-column designs generated from contractions (Piepho, Williams) Row-column designs play an important role in applications where two orthogonal sources of error need to be controlled for by blocking. Field or greenhouse experiments, in which experimental units are a
November 26, 2025 at 4:28 PM
link 📈🤖
Rectangular augmented row-column designs generated from contractions (Piepho, Williams) Row-column designs play an important role in applications where two orthogonal sources of error need to be controlled for by blocking. Field or greenhouse experiments, in which experimental units are a
Rectangular augmented row-column designs generated from contractions (Piepho, Williams) Row-column designs play an important role in applications where two orthogonal sources of error need to be controlled for by blocking. Field or greenhouse experiments, in which experimental units are a
link 📈🤖
Hierarchical Causal Structure Learning (Hermes, Heerwaarden, Eeuwijk et al) Traditional statistical approaches primarily aim to model associations between variables, but many scientific and practical questions require causal methods instead. These approaches rely on assumptions about an u
Hierarchical Causal Structure Learning (Hermes, Heerwaarden, Eeuwijk et al) Traditional statistical approaches primarily aim to model associations between variables, but many scientific and practical questions require causal methods instead. These approaches rely on assumptions about an u
November 26, 2025 at 4:27 PM
link 📈🤖
Hierarchical Causal Structure Learning (Hermes, Heerwaarden, Eeuwijk et al) Traditional statistical approaches primarily aim to model associations between variables, but many scientific and practical questions require causal methods instead. These approaches rely on assumptions about an u
Hierarchical Causal Structure Learning (Hermes, Heerwaarden, Eeuwijk et al) Traditional statistical approaches primarily aim to model associations between variables, but many scientific and practical questions require causal methods instead. These approaches rely on assumptions about an u
link 📈🤖
Dependence-Aware False Discovery Rate Control in Two-Sided Gaussian Mean Testing (Ghosh, Sarkar) This paper develops a general framework for controlling the false discovery rate (FDR) in multiple testing of Gaussian means against two-sided alternatives. The widely used Benjamini-Hochberg
Dependence-Aware False Discovery Rate Control in Two-Sided Gaussian Mean Testing (Ghosh, Sarkar) This paper develops a general framework for controlling the false discovery rate (FDR) in multiple testing of Gaussian means against two-sided alternatives. The widely used Benjamini-Hochberg
November 26, 2025 at 4:23 PM
link 📈🤖
Dependence-Aware False Discovery Rate Control in Two-Sided Gaussian Mean Testing (Ghosh, Sarkar) This paper develops a general framework for controlling the false discovery rate (FDR) in multiple testing of Gaussian means against two-sided alternatives. The widely used Benjamini-Hochberg
Dependence-Aware False Discovery Rate Control in Two-Sided Gaussian Mean Testing (Ghosh, Sarkar) This paper develops a general framework for controlling the false discovery rate (FDR) in multiple testing of Gaussian means against two-sided alternatives. The widely used Benjamini-Hochberg
link 📈🤖
Institutional Learning and Volatility Transmission in ASEAN Equity Markets: A Network-Integrated Regime-Dependent Approach (Yang) This paper investigates how institutional learning and regional spillovers shape volatility dynamics in ASEAN equity markets. Using daily data for Indonesia, M
Institutional Learning and Volatility Transmission in ASEAN Equity Markets: A Network-Integrated Regime-Dependent Approach (Yang) This paper investigates how institutional learning and regional spillovers shape volatility dynamics in ASEAN equity markets. Using daily data for Indonesia, M
November 26, 2025 at 4:20 PM
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Institutional Learning and Volatility Transmission in ASEAN Equity Markets: A Network-Integrated Regime-Dependent Approach (Yang) This paper investigates how institutional learning and regional spillovers shape volatility dynamics in ASEAN equity markets. Using daily data for Indonesia, M
Institutional Learning and Volatility Transmission in ASEAN Equity Markets: A Network-Integrated Regime-Dependent Approach (Yang) This paper investigates how institutional learning and regional spillovers shape volatility dynamics in ASEAN equity markets. Using daily data for Indonesia, M
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Threshold Tensor Factor Model in CP Form (Bolivar, Chen, Han) This paper proposes a new Threshold Tensor Factor Model in Canonical Polyadic (CP) form for tensor time series. By integrating a thresholding autoregressive structure for the latent factor process into the tensor factor model i
Threshold Tensor Factor Model in CP Form (Bolivar, Chen, Han) This paper proposes a new Threshold Tensor Factor Model in Canonical Polyadic (CP) form for tensor time series. By integrating a thresholding autoregressive structure for the latent factor process into the tensor factor model i
November 26, 2025 at 4:19 PM
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Threshold Tensor Factor Model in CP Form (Bolivar, Chen, Han) This paper proposes a new Threshold Tensor Factor Model in Canonical Polyadic (CP) form for tensor time series. By integrating a thresholding autoregressive structure for the latent factor process into the tensor factor model i
Threshold Tensor Factor Model in CP Form (Bolivar, Chen, Han) This paper proposes a new Threshold Tensor Factor Model in Canonical Polyadic (CP) form for tensor time series. By integrating a thresholding autoregressive structure for the latent factor process into the tensor factor model i
link 📈🤖
Differentially Private Computation of the Gini Index for Income Inequality (Lan, Reiter) The Gini index is a widely reported measure of income inequality. In some settings, the underlying data used to compute the Gini index are confidential. The organization charged with reporting the Gin
Differentially Private Computation of the Gini Index for Income Inequality (Lan, Reiter) The Gini index is a widely reported measure of income inequality. In some settings, the underlying data used to compute the Gini index are confidential. The organization charged with reporting the Gin
November 26, 2025 at 4:17 PM
link 📈🤖
Differentially Private Computation of the Gini Index for Income Inequality (Lan, Reiter) The Gini index is a widely reported measure of income inequality. In some settings, the underlying data used to compute the Gini index are confidential. The organization charged with reporting the Gin
Differentially Private Computation of the Gini Index for Income Inequality (Lan, Reiter) The Gini index is a widely reported measure of income inequality. In some settings, the underlying data used to compute the Gini index are confidential. The organization charged with reporting the Gin
link 📈🤖
Order Selection in Vector Autoregression by Mean Square Information Criterion (Hellstern, Shojaie) Vector autoregressive (VAR) processes are ubiquitously used in economics, finance, and biology. Order selection is an essential step in fitting VAR models. While many order selection methods
Order Selection in Vector Autoregression by Mean Square Information Criterion (Hellstern, Shojaie) Vector autoregressive (VAR) processes are ubiquitously used in economics, finance, and biology. Order selection is an essential step in fitting VAR models. While many order selection methods
November 26, 2025 at 4:14 PM
link 📈🤖
Order Selection in Vector Autoregression by Mean Square Information Criterion (Hellstern, Shojaie) Vector autoregressive (VAR) processes are ubiquitously used in economics, finance, and biology. Order selection is an essential step in fitting VAR models. While many order selection methods
Order Selection in Vector Autoregression by Mean Square Information Criterion (Hellstern, Shojaie) Vector autoregressive (VAR) processes are ubiquitously used in economics, finance, and biology. Order selection is an essential step in fitting VAR models. While many order selection methods
link 📈🤖
Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis (Yang, Gamalo, Fu) Randomized controlled trials (RCTs) have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity. Stu
Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis (Yang, Gamalo, Fu) Randomized controlled trials (RCTs) have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity. Stu
November 26, 2025 at 4:12 PM
link 📈🤖
Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis (Yang, Gamalo, Fu) Randomized controlled trials (RCTs) have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity. Stu
Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis (Yang, Gamalo, Fu) Randomized controlled trials (RCTs) have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity. Stu