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 📈🤖
Fast, effective, and coherent time series modeling using the sparsity-ranked lasso () arXiv:2211.01492v2 Announce Type: replace
Abstract: The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet
Fast, effective, and coherent time series modeling using the sparsity-ranked lasso () arXiv:2211.01492v2 Announce Type: replace
Abstract: The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet
November 11, 2025 at 3:26 AM
link 📈🤖
Fast, effective, and coherent time series modeling using the sparsity-ranked lasso () arXiv:2211.01492v2 Announce Type: replace
Abstract: The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet
Fast, effective, and coherent time series modeling using the sparsity-ranked lasso () arXiv:2211.01492v2 Announce Type: replace
Abstract: The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet
link 📈🤖
Semi-supervised Vertex Hunting, with Applications in Network and Text Analysis (Jiang, Ke) Vertex hunting (VH) is the task of estimating a simplex from noisy data points and has many applications in areas such as network and text analysis. We introduce a new variant, semi-supervised verte
Semi-supervised Vertex Hunting, with Applications in Network and Text Analysis (Jiang, Ke) Vertex hunting (VH) is the task of estimating a simplex from noisy data points and has many applications in areas such as network and text analysis. We introduce a new variant, semi-supervised verte
November 11, 2025 at 1:34 AM
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Semi-supervised Vertex Hunting, with Applications in Network and Text Analysis (Jiang, Ke) Vertex hunting (VH) is the task of estimating a simplex from noisy data points and has many applications in areas such as network and text analysis. We introduce a new variant, semi-supervised verte
Semi-supervised Vertex Hunting, with Applications in Network and Text Analysis (Jiang, Ke) Vertex hunting (VH) is the task of estimating a simplex from noisy data points and has many applications in areas such as network and text analysis. We introduce a new variant, semi-supervised verte
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A bivariate two-state Markov modulated Poisson process for failure modelling () Motivated by a real failure dataset in a two-dimensional context, this paper
presents an extension of the Markov modulated Poisson process (MMPP) to two
dimensions. The one-dimensional MMPP has been proposed f
A bivariate two-state Markov modulated Poisson process for failure modelling () Motivated by a real failure dataset in a two-dimensional context, this paper
presents an extension of the Markov modulated Poisson process (MMPP) to two
dimensions. The one-dimensional MMPP has been proposed f
November 10, 2025 at 10:06 PM
link 📈🤖
A bivariate two-state Markov modulated Poisson process for failure modelling () Motivated by a real failure dataset in a two-dimensional context, this paper
presents an extension of the Markov modulated Poisson process (MMPP) to two
dimensions. The one-dimensional MMPP has been proposed f
A bivariate two-state Markov modulated Poisson process for failure modelling () Motivated by a real failure dataset in a two-dimensional context, this paper
presents an extension of the Markov modulated Poisson process (MMPP) to two
dimensions. The one-dimensional MMPP has been proposed f
link 📈🤖
A goodness-of-fit test for regression models with spatially correlated errors () The problem of assessing a parametric regression model in the presence of spatial correlation is addressed in this work. For that purpose, a goodness-of-fit test based on a $L_2$-distance comparing a parametr
A goodness-of-fit test for regression models with spatially correlated errors () The problem of assessing a parametric regression model in the presence of spatial correlation is addressed in this work. For that purpose, a goodness-of-fit test based on a $L_2$-distance comparing a parametr
November 10, 2025 at 7:05 PM
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A goodness-of-fit test for regression models with spatially correlated errors () The problem of assessing a parametric regression model in the presence of spatial correlation is addressed in this work. For that purpose, a goodness-of-fit test based on a $L_2$-distance comparing a parametr
A goodness-of-fit test for regression models with spatially correlated errors () The problem of assessing a parametric regression model in the presence of spatial correlation is addressed in this work. For that purpose, a goodness-of-fit test based on a $L_2$-distance comparing a parametr
link 📈🤖
Externally Valid Policy Choice (Adjaho, Christensen) We consider the problem of estimating personalized treatment policies that are "externally valid" or "generalizable": they perform well in target populations that differ from the experimental (or training) population from which the data
Externally Valid Policy Choice (Adjaho, Christensen) We consider the problem of estimating personalized treatment policies that are "externally valid" or "generalizable": they perform well in target populations that differ from the experimental (or training) population from which the data
November 10, 2025 at 4:53 PM
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Externally Valid Policy Choice (Adjaho, Christensen) We consider the problem of estimating personalized treatment policies that are "externally valid" or "generalizable": they perform well in target populations that differ from the experimental (or training) population from which the data
Externally Valid Policy Choice (Adjaho, Christensen) We consider the problem of estimating personalized treatment policies that are "externally valid" or "generalizable": they perform well in target populations that differ from the experimental (or training) population from which the data
link 📈🤖
Nonparametric Inference on Unlabeled Histograms (Ma, Yang) Statistical inference on histograms and frequency counts plays a central role in categorical data analysis. Moving beyond classical methods that directly analyze labeled frequencies, we introduce a framework that models the multis
Nonparametric Inference on Unlabeled Histograms (Ma, Yang) Statistical inference on histograms and frequency counts plays a central role in categorical data analysis. Moving beyond classical methods that directly analyze labeled frequencies, we introduce a framework that models the multis
November 10, 2025 at 4:51 PM
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Nonparametric Inference on Unlabeled Histograms (Ma, Yang) Statistical inference on histograms and frequency counts plays a central role in categorical data analysis. Moving beyond classical methods that directly analyze labeled frequencies, we introduce a framework that models the multis
Nonparametric Inference on Unlabeled Histograms (Ma, Yang) Statistical inference on histograms and frequency counts plays a central role in categorical data analysis. Moving beyond classical methods that directly analyze labeled frequencies, we introduce a framework that models the multis
link 📈🤖
Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning (Tanaka) In this study, a scalable online kernel learning framework is proposed for estimating bidirectional causal effects in systems characterized by mutual dependence and heteroskedasticity. Traditional cau
Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning (Tanaka) In this study, a scalable online kernel learning framework is proposed for estimating bidirectional causal effects in systems characterized by mutual dependence and heteroskedasticity. Traditional cau
November 10, 2025 at 4:46 PM
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Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning (Tanaka) In this study, a scalable online kernel learning framework is proposed for estimating bidirectional causal effects in systems characterized by mutual dependence and heteroskedasticity. Traditional cau
Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning (Tanaka) In this study, a scalable online kernel learning framework is proposed for estimating bidirectional causal effects in systems characterized by mutual dependence and heteroskedasticity. Traditional cau
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Insights into Tail-Based and Order Statistics (Almani) Heavy-tailed phenomena appear across diverse domains --from wealth and firm sizes in economics to network traffic, biological systems, and physical processes-- characterized by the disproportionate influence of extreme values. These d
Insights into Tail-Based and Order Statistics (Almani) Heavy-tailed phenomena appear across diverse domains --from wealth and firm sizes in economics to network traffic, biological systems, and physical processes-- characterized by the disproportionate influence of extreme values. These d
November 10, 2025 at 4:44 PM
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Insights into Tail-Based and Order Statistics (Almani) Heavy-tailed phenomena appear across diverse domains --from wealth and firm sizes in economics to network traffic, biological systems, and physical processes-- characterized by the disproportionate influence of extreme values. These d
Insights into Tail-Based and Order Statistics (Almani) Heavy-tailed phenomena appear across diverse domains --from wealth and firm sizes in economics to network traffic, biological systems, and physical processes-- characterized by the disproportionate influence of extreme values. These d
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Function on Scalar Regression with Complex Survey Designs (Koffman, Gao, Zhou et al) Large health surveys increasingly collect high-dimensional functional data from wearable devices, and function on scalar regression (FoSR) is often used to quantify the relationship between these function
Function on Scalar Regression with Complex Survey Designs (Koffman, Gao, Zhou et al) Large health surveys increasingly collect high-dimensional functional data from wearable devices, and function on scalar regression (FoSR) is often used to quantify the relationship between these function
November 10, 2025 at 4:39 PM
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Function on Scalar Regression with Complex Survey Designs (Koffman, Gao, Zhou et al) Large health surveys increasingly collect high-dimensional functional data from wearable devices, and function on scalar regression (FoSR) is often used to quantify the relationship between these function
Function on Scalar Regression with Complex Survey Designs (Koffman, Gao, Zhou et al) Large health surveys increasingly collect high-dimensional functional data from wearable devices, and function on scalar regression (FoSR) is often used to quantify the relationship between these function
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Conditioning on posterior samples for flexible frequentist goodness-of-fit testing (Bhaduri, Bhattacharyya, Barber et al) Tests of goodness of fit are used in nearly every domain where statistics is applied. One powerful and flexible approach is to sample artificial data sets that are exc
Conditioning on posterior samples for flexible frequentist goodness-of-fit testing (Bhaduri, Bhattacharyya, Barber et al) Tests of goodness of fit are used in nearly every domain where statistics is applied. One powerful and flexible approach is to sample artificial data sets that are exc
November 10, 2025 at 4:35 PM
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Conditioning on posterior samples for flexible frequentist goodness-of-fit testing (Bhaduri, Bhattacharyya, Barber et al) Tests of goodness of fit are used in nearly every domain where statistics is applied. One powerful and flexible approach is to sample artificial data sets that are exc
Conditioning on posterior samples for flexible frequentist goodness-of-fit testing (Bhaduri, Bhattacharyya, Barber et al) Tests of goodness of fit are used in nearly every domain where statistics is applied. One powerful and flexible approach is to sample artificial data sets that are exc
link 📈🤖
Do Test Scores Help Teachers Give Better Track Advice to Students? A Principal Stratification Analysis (Ichino, Mealli, Viviens) We study whether access to standardized test scores improves the quality of teachers' secondary school track recommendations, using Dutch data and a metric base
Do Test Scores Help Teachers Give Better Track Advice to Students? A Principal Stratification Analysis (Ichino, Mealli, Viviens) We study whether access to standardized test scores improves the quality of teachers' secondary school track recommendations, using Dutch data and a metric base
November 10, 2025 at 4:32 PM
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Do Test Scores Help Teachers Give Better Track Advice to Students? A Principal Stratification Analysis (Ichino, Mealli, Viviens) We study whether access to standardized test scores improves the quality of teachers' secondary school track recommendations, using Dutch data and a metric base
Do Test Scores Help Teachers Give Better Track Advice to Students? A Principal Stratification Analysis (Ichino, Mealli, Viviens) We study whether access to standardized test scores improves the quality of teachers' secondary school track recommendations, using Dutch data and a metric base
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On linkage bias-correction for estimators using iterated bootstraps (Tam, Wang, Rambaldi et al) By amalgamating data from disparate sources, the resulting integrated dataset becomes a valuable resource for statistical analysis. In probabilistic record linkage, the effectiveness of such in
On linkage bias-correction for estimators using iterated bootstraps (Tam, Wang, Rambaldi et al) By amalgamating data from disparate sources, the resulting integrated dataset becomes a valuable resource for statistical analysis. In probabilistic record linkage, the effectiveness of such in
November 10, 2025 at 4:30 PM
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On linkage bias-correction for estimators using iterated bootstraps (Tam, Wang, Rambaldi et al) By amalgamating data from disparate sources, the resulting integrated dataset becomes a valuable resource for statistical analysis. In probabilistic record linkage, the effectiveness of such in
On linkage bias-correction for estimators using iterated bootstraps (Tam, Wang, Rambaldi et al) By amalgamating data from disparate sources, the resulting integrated dataset becomes a valuable resource for statistical analysis. In probabilistic record linkage, the effectiveness of such in
link 📈🤖
Training and Testing with Multiple Splits: A Central Limit Theorem for Split-Sample Estimators (Fava) As predictive algorithms grow in popularity, using the same dataset to both train and test a new model has become routine across research, policy, and industry. Sample-splitting attains v
Training and Testing with Multiple Splits: A Central Limit Theorem for Split-Sample Estimators (Fava) As predictive algorithms grow in popularity, using the same dataset to both train and test a new model has become routine across research, policy, and industry. Sample-splitting attains v
November 10, 2025 at 4:25 PM
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Training and Testing with Multiple Splits: A Central Limit Theorem for Split-Sample Estimators (Fava) As predictive algorithms grow in popularity, using the same dataset to both train and test a new model has become routine across research, policy, and industry. Sample-splitting attains v
Training and Testing with Multiple Splits: A Central Limit Theorem for Split-Sample Estimators (Fava) As predictive algorithms grow in popularity, using the same dataset to both train and test a new model has become routine across research, policy, and industry. Sample-splitting attains v
link 📈🤖
Words Matter: Forecasting Economic Downside Risks with Corporate Textual Data (Isler) Accurate forecasting of downside risks to economic growth is critically important for policymakers and financial institutions, particularly in the wake of recent economic crises. This paper extends the G
Words Matter: Forecasting Economic Downside Risks with Corporate Textual Data (Isler) Accurate forecasting of downside risks to economic growth is critically important for policymakers and financial institutions, particularly in the wake of recent economic crises. This paper extends the G
November 10, 2025 at 4:23 PM
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Words Matter: Forecasting Economic Downside Risks with Corporate Textual Data (Isler) Accurate forecasting of downside risks to economic growth is critically important for policymakers and financial institutions, particularly in the wake of recent economic crises. This paper extends the G
Words Matter: Forecasting Economic Downside Risks with Corporate Textual Data (Isler) Accurate forecasting of downside risks to economic growth is critically important for policymakers and financial institutions, particularly in the wake of recent economic crises. This paper extends the G
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Clustering in Networks with Time-varying Nodal Attributes (Kei, Padilla, Killick et al) This manuscript studies nodal clustering in graphs having a time series at each node. The framework includes priors for low-dimensional representations and a decoder that bridges the latent representat
Clustering in Networks with Time-varying Nodal Attributes (Kei, Padilla, Killick et al) This manuscript studies nodal clustering in graphs having a time series at each node. The framework includes priors for low-dimensional representations and a decoder that bridges the latent representat
November 10, 2025 at 4:18 PM
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Clustering in Networks with Time-varying Nodal Attributes (Kei, Padilla, Killick et al) This manuscript studies nodal clustering in graphs having a time series at each node. The framework includes priors for low-dimensional representations and a decoder that bridges the latent representat
Clustering in Networks with Time-varying Nodal Attributes (Kei, Padilla, Killick et al) This manuscript studies nodal clustering in graphs having a time series at each node. The framework includes priors for low-dimensional representations and a decoder that bridges the latent representat
link 📈🤖
Inference for the Extended Functional Cox Model: A UK Biobank Case Study (Cui, Zhao, Crainiceanu) Multiple studies have shown that scalar summaries of objectively measured physical activity (PA) using accelerometers are the strongest predictors of mortality, outperforming all traditional
Inference for the Extended Functional Cox Model: A UK Biobank Case Study (Cui, Zhao, Crainiceanu) Multiple studies have shown that scalar summaries of objectively measured physical activity (PA) using accelerometers are the strongest predictors of mortality, outperforming all traditional
November 10, 2025 at 4:14 PM
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Inference for the Extended Functional Cox Model: A UK Biobank Case Study (Cui, Zhao, Crainiceanu) Multiple studies have shown that scalar summaries of objectively measured physical activity (PA) using accelerometers are the strongest predictors of mortality, outperforming all traditional
Inference for the Extended Functional Cox Model: A UK Biobank Case Study (Cui, Zhao, Crainiceanu) Multiple studies have shown that scalar summaries of objectively measured physical activity (PA) using accelerometers are the strongest predictors of mortality, outperforming all traditional
link 📈🤖
An Integrative Approach for Subtyping Mental Disorders Using Multimodal Data (Zhao, Wang, LIu) Understanding the biological and behavioral heterogeneity underlying psychiatric disorders is critical for advancing precision diagnosis, treatment, and prevention. This paper addresses the scie
An Integrative Approach for Subtyping Mental Disorders Using Multimodal Data (Zhao, Wang, LIu) Understanding the biological and behavioral heterogeneity underlying psychiatric disorders is critical for advancing precision diagnosis, treatment, and prevention. This paper addresses the scie
November 10, 2025 at 4:11 PM
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An Integrative Approach for Subtyping Mental Disorders Using Multimodal Data (Zhao, Wang, LIu) Understanding the biological and behavioral heterogeneity underlying psychiatric disorders is critical for advancing precision diagnosis, treatment, and prevention. This paper addresses the scie
An Integrative Approach for Subtyping Mental Disorders Using Multimodal Data (Zhao, Wang, LIu) Understanding the biological and behavioral heterogeneity underlying psychiatric disorders is critical for advancing precision diagnosis, treatment, and prevention. This paper addresses the scie
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The Kaplan-Meier Estimator as a Sum over Units (Tichy) A sum-wise formulation is proposed for the Kaplan-Meier product limit estimator of partially right-censored survival data. The derived representation permits to write the population's estimator as a sum over its individual units' semi
The Kaplan-Meier Estimator as a Sum over Units (Tichy) A sum-wise formulation is proposed for the Kaplan-Meier product limit estimator of partially right-censored survival data. The derived representation permits to write the population's estimator as a sum over its individual units' semi
November 10, 2025 at 4:07 PM
link 📈🤖
The Kaplan-Meier Estimator as a Sum over Units (Tichy) A sum-wise formulation is proposed for the Kaplan-Meier product limit estimator of partially right-censored survival data. The derived representation permits to write the population's estimator as a sum over its individual units' semi
The Kaplan-Meier Estimator as a Sum over Units (Tichy) A sum-wise formulation is proposed for the Kaplan-Meier product limit estimator of partially right-censored survival data. The derived representation permits to write the population's estimator as a sum over its individual units' semi
link 📈🤖
Bayesian spatio-temporal modelling for infectious disease outbreak detection (Adeoye, Didelot, Spencer) The Bayesian analysis of infectious disease surveillance data from multiple locations typically involves building and fitting a spatio-temporal model of how the disease spreads in the s
Bayesian spatio-temporal modelling for infectious disease outbreak detection (Adeoye, Didelot, Spencer) The Bayesian analysis of infectious disease surveillance data from multiple locations typically involves building and fitting a spatio-temporal model of how the disease spreads in the s
November 10, 2025 at 3:30 AM
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Bayesian spatio-temporal modelling for infectious disease outbreak detection (Adeoye, Didelot, Spencer) The Bayesian analysis of infectious disease surveillance data from multiple locations typically involves building and fitting a spatio-temporal model of how the disease spreads in the s
Bayesian spatio-temporal modelling for infectious disease outbreak detection (Adeoye, Didelot, Spencer) The Bayesian analysis of infectious disease surveillance data from multiple locations typically involves building and fitting a spatio-temporal model of how the disease spreads in the s
link 📈🤖
Multivariate Interval-Valued Models in Frequentist and Bayesian Schemes () arXiv:2405.06635v1 Announce Type: new
Abstract: In recent years, addressing the challenges posed by massive datasets has led researchers to explore aggregated data, particularly leveraging interval-valued data, ak
Multivariate Interval-Valued Models in Frequentist and Bayesian Schemes () arXiv:2405.06635v1 Announce Type: new
Abstract: In recent years, addressing the challenges posed by massive datasets has led researchers to explore aggregated data, particularly leveraging interval-valued data, ak
November 10, 2025 at 1:34 AM
link 📈🤖
Multivariate Interval-Valued Models in Frequentist and Bayesian Schemes () arXiv:2405.06635v1 Announce Type: new
Abstract: In recent years, addressing the challenges posed by massive datasets has led researchers to explore aggregated data, particularly leveraging interval-valued data, ak
Multivariate Interval-Valued Models in Frequentist and Bayesian Schemes () arXiv:2405.06635v1 Announce Type: new
Abstract: In recent years, addressing the challenges posed by massive datasets has led researchers to explore aggregated data, particularly leveraging interval-valued data, ak
link 📈🤖
NEST: Neural Estimation by Sequential Testing () arXiv:2405.04226v1 Announce Type: new
Abstract: Adaptive psychophysical procedures aim to increase the efficiency and reliability of measurements. With increasing stimulus and experiment complexity in the last decade, estimating multi-dime
NEST: Neural Estimation by Sequential Testing () arXiv:2405.04226v1 Announce Type: new
Abstract: Adaptive psychophysical procedures aim to increase the efficiency and reliability of measurements. With increasing stimulus and experiment complexity in the last decade, estimating multi-dime
November 9, 2025 at 10:05 PM
link 📈🤖
NEST: Neural Estimation by Sequential Testing () arXiv:2405.04226v1 Announce Type: new
Abstract: Adaptive psychophysical procedures aim to increase the efficiency and reliability of measurements. With increasing stimulus and experiment complexity in the last decade, estimating multi-dime
NEST: Neural Estimation by Sequential Testing () arXiv:2405.04226v1 Announce Type: new
Abstract: Adaptive psychophysical procedures aim to increase the efficiency and reliability of measurements. With increasing stimulus and experiment complexity in the last decade, estimating multi-dime
link 📈🤖
Robust Functional Data Analysis for Stochastic Evolution Equations in Infinite Dimensions () arXiv:2401.16286v2 Announce Type: replace
Abstract: We develop an asymptotic theory for the jump robust measurement of covariations in the context of stochastic evolution equation in infinite dim
Robust Functional Data Analysis for Stochastic Evolution Equations in Infinite Dimensions () arXiv:2401.16286v2 Announce Type: replace
Abstract: We develop an asymptotic theory for the jump robust measurement of covariations in the context of stochastic evolution equation in infinite dim
November 9, 2025 at 7:04 PM
link 📈🤖
Robust Functional Data Analysis for Stochastic Evolution Equations in Infinite Dimensions () arXiv:2401.16286v2 Announce Type: replace
Abstract: We develop an asymptotic theory for the jump robust measurement of covariations in the context of stochastic evolution equation in infinite dim
Robust Functional Data Analysis for Stochastic Evolution Equations in Infinite Dimensions () arXiv:2401.16286v2 Announce Type: replace
Abstract: We develop an asymptotic theory for the jump robust measurement of covariations in the context of stochastic evolution equation in infinite dim
link 📈🤖
Estimating Trustworthy and Safe Optimal Treatment Regimes () Recent statistical and reinforcement learning methods have significantly
advanced patient care strategies. However, these approaches face substantial
challenges in high-stakes contexts, including missing data, inherent
stochasti
Estimating Trustworthy and Safe Optimal Treatment Regimes () Recent statistical and reinforcement learning methods have significantly
advanced patient care strategies. However, these approaches face substantial
challenges in high-stakes contexts, including missing data, inherent
stochasti
November 9, 2025 at 4:05 PM
link 📈🤖
Estimating Trustworthy and Safe Optimal Treatment Regimes () Recent statistical and reinforcement learning methods have significantly
advanced patient care strategies. However, these approaches face substantial
challenges in high-stakes contexts, including missing data, inherent
stochasti
Estimating Trustworthy and Safe Optimal Treatment Regimes () Recent statistical and reinforcement learning methods have significantly
advanced patient care strategies. However, these approaches face substantial
challenges in high-stakes contexts, including missing data, inherent
stochasti
link 📈🤖
A Deep Learning Method for Comparing Bayesian Hierarchical Models () Bayesian model comparison (BMC) offers a principled approach for assessing
the relative merits of competing computational models and propagating
uncertainty into model selection decisions. However, BMC is often intractab
A Deep Learning Method for Comparing Bayesian Hierarchical Models () Bayesian model comparison (BMC) offers a principled approach for assessing
the relative merits of competing computational models and propagating
uncertainty into model selection decisions. However, BMC is often intractab
November 9, 2025 at 3:25 AM
link 📈🤖
A Deep Learning Method for Comparing Bayesian Hierarchical Models () Bayesian model comparison (BMC) offers a principled approach for assessing
the relative merits of competing computational models and propagating
uncertainty into model selection decisions. However, BMC is often intractab
A Deep Learning Method for Comparing Bayesian Hierarchical Models () Bayesian model comparison (BMC) offers a principled approach for assessing
the relative merits of competing computational models and propagating
uncertainty into model selection decisions. However, BMC is often intractab
link 📈🤖
Quantile Fourier Transform, Quantile Series, and Nonparametric Estimation of Quantile Spectra (Li) A nonparametric method is proposed for estimating the quantile spectra and cross-spectra introduced in Li (2012; 2014) as bivariate functions of frequency and quantile level. The method is b
Quantile Fourier Transform, Quantile Series, and Nonparametric Estimation of Quantile Spectra (Li) A nonparametric method is proposed for estimating the quantile spectra and cross-spectra introduced in Li (2012; 2014) as bivariate functions of frequency and quantile level. The method is b
November 9, 2025 at 1:33 AM
link 📈🤖
Quantile Fourier Transform, Quantile Series, and Nonparametric Estimation of Quantile Spectra (Li) A nonparametric method is proposed for estimating the quantile spectra and cross-spectra introduced in Li (2012; 2014) as bivariate functions of frequency and quantile level. The method is b
Quantile Fourier Transform, Quantile Series, and Nonparametric Estimation of Quantile Spectra (Li) A nonparametric method is proposed for estimating the quantile spectra and cross-spectra introduced in Li (2012; 2014) as bivariate functions of frequency and quantile level. The method is b