Wenxin Jiang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Wenxin Jiang has a total of 18 co-authors. Here are the top co-authors by number of shared publications:
Martin A. Tanner
Northwestern University
3 shared publications- Martin A. Tanner
Institute of Finance (Adjunct) Department of Statistics Institute for Quantitative Social Science Department of Statistics Shandong University Northwestern University Harvard University Northwestern University
3 shared publications - Allen Schmaltz
Institute of Finance (Adjunct) Department of Statistics Institute for Quantitative Social Science Department of Statistics Shandong University Northwestern University Harvard University Northwestern University
2 shared publications - Cheng Li
Department of Statistics and Data Science National University of Singapore
2 shared publications - Gary King
Institute for Quantitative Social Science, Harvard University, Cambridge, USA
2 shared publications - Haisheng Ji
Purple Mountain Observatory, Nanjing 210042, China
2 shared publications
Latest Publications
- Open Access
- 23 October 2026
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Knowledge Discovery From Data
Statistical Views on F beta and other Performance Metrics for Data Mining
We give analytic formulas of asymptotic confidence intervals (CIs) for \(F_{\beta}\) metric with \(\beta\) not necessarily \(1\) . They are useful both for fast computation and also for sample size planning, in order to have tight enough CIs cover the true parameter enough often. We also study joint confidence intervals to be able to make multiple valid statements simultaneously and after looking at the CI results, with a high enough probability asymptotically for large data. The approach can be generalized in several ways and can be used for other performance metrics in data mining, including, e.g., classification accuracy, precision, recall, F metrics, and Jaccard index. We also consider a variance correction method for improving finite sample performance, the Bonferroni bound for faster computation, some variants of bootstraps, and extension to multiclass problems.- 23 October 2026
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Knowledge Discovery From Data
Open AccessStatistical Views on F beta and other Performance Metrics for Data Mining
We give analytic formulas of asymptotic confidence intervals (CIs) for \(F_{\beta}\) metric with \(\beta\) not necessarily \(1\) . They are useful both for fast computation and also for sample size planning, in order to have tight enough CIs cover the true parameter enough often. We also study joint confidence intervals to be able to make multiple valid statements simultaneously and after looking at the CI results, with a high enough probability asymptotically for large data. The approach can be generalized in several ways and can be used for other performance metrics in data mining, including, e.g., classification accuracy, precision, recall, F metrics, and Jaccard index. We also consider a variance correction method for improving finite sample performance, the Bonferroni bound for faster computation, some variants of bootstraps, and extension to multiclass problems. - Open Access
Bayesian Knowledge Infusion for Studying Historical Sunspot Numbers
A scientific method that proposes a value Y to estimate a target value is often subject to some level of uncertainty. In the Bayesian framework, the level of uncertainty can be measured by the width of the interval, which is the range of the middle of the ranked values sampled from the posterior distribution . This paper considers Bayesian knowledge infusion (BKI) to reduce the uncertainty of the posterior distribution based on additional knowledge that an event A happens. BKI is achieved by using a conditional prior distribution in the Bayes theorem, assuming that given the true , its error-contaminated value Y is independent of event A. We use two examples to illustrate how to study whether or not it is possible to reduce uncertainty from 14C reconstruction (Y) of the annual sunspot number (SSN) () by infusing additional information (A) using BKI. Information (A) that SSN is from a year that has a Far Eastern record of naked eye sunspots is found to be not so effective in reducing the uncertainty. In contrast, information that SSN is from a year at a cycle minimum is found to be very effective, producing much narrower 68% intervals. The resulting Bayesian point estimates of SSN (the posterior medians of ) are cross-validated and tested on a subset of telescopically observed SSNs that were unused in the process of Bayes computation.Open AccessBayesian Knowledge Infusion for Studying Historical Sunspot Numbers
A scientific method that proposes a value Y to estimate a target value is often subject to some level of uncertainty. In the Bayesian framework, the level of uncertainty can be measured by the width of the interval, which is the range of the middle of the ranked values sampled from the posterior distribution . This paper considers Bayesian knowledge infusion (BKI) to reduce the uncertainty of the posterior distribution based on additional knowledge that an event A happens. BKI is achieved by using a conditional prior distribution in the Bayes theorem, assuming that given the true , its error-contaminated value Y is independent of event A. We use two examples to illustrate how to study whether or not it is possible to reduce uncertainty from 14C reconstruction (Y) of the annual sunspot number (SSN) () by infusing additional information (A) using BKI. Information (A) that SSN is from a year that has a Far Eastern record of naked eye sunspots is found to be not so effective in reducing the uncertainty. In contrast, information that SSN is from a year at a cycle minimum is found to be very effective, producing much narrower 68% intervals. The resulting Bayesian point estimates of SSN (the posterior medians of ) are cross-validated and tested on a subset of telescopically observed SSNs that were unused in the process of Bayes computation. - Open Access
Conditional variable screening via ordinary least squares projection
In this article, we propose a novel variable screening method for linear models named as conditional screening via ordinary least squares projection (COLP). COLP can take advantage of prior knowledge concerning certain active predictors by eliminating the adverse impact of their coefficients in the estimation of remaining ones and thus significantly enhance the screening accuracy. We prove its sure-screening property under reasonable assumptions and demonstrate its utility in an application to a leukemia dataset. Moreover, based on the conditional approach, we introduce an iterative algorithm named as forward screening via ordinary least squares projection (FOLP), which not only could exploit the prior information more effectively, but also has promising performance when no prior knowledge is available using a data-driven conditioning set. Extensive simulation studies are carried out to demonstrate the competence of both proposed methods.
Open AccessConditional variable screening via ordinary least squares projection
In this article, we propose a novel variable screening method for linear models named as conditional screening via ordinary least squares projection (COLP). COLP can take advantage of prior knowledge concerning certain active predictors by eliminating the adverse impact of their coefficients in the estimation of remaining ones and thus significantly enhance the screening accuracy. We prove its sure-screening property under reasonable assumptions and demonstrate its utility in an application to a leukemia dataset. Moreover, based on the conditional approach, we introduce an iterative algorithm named as forward screening via ordinary least squares projection (FOLP), which not only could exploit the prior information more effectively, but also has promising performance when no prior knowledge is available using a data-driven conditioning set. Extensive simulation studies are carried out to demonstrate the competence of both proposed methods.
- Open Access
On the sure screening properties of iteratively sure independence screening algorithms
Fan and Lv (2008) proposed the path-breaking theory of sure independence screening (SIS) and an iterative algorithm (ISIS) to effectively reduce the predictor dimension for further variable selection approaches. Fan et al. (2009) extended ISIS to generalized linear models and introduced the Vanilla ISIS (Van-ISIS) algorithm, allowing selected predictors to be screened out in upcoming iterations. The success of SIS depends on its sure screening property, which was obtained by Fan and Lv (2008) under the marginal correlation assumption. However, despite wide applications of ISIS and Van-ISIS in various scientific fields, their sure screening properties have not been proved during the past decade. To fill this gap, we prove the sure screening properties of three different types of iterative algorithms for linear models without relying on the marginal correlation assumption, where ISIS and Van-ISIS can be regarded as two special cases of them.
Open AccessOn the sure screening properties of iteratively sure independence screening algorithms
Fan and Lv (2008) proposed the path-breaking theory of sure independence screening (SIS) and an iterative algorithm (ISIS) to effectively reduce the predictor dimension for further variable selection approaches. Fan et al. (2009) extended ISIS to generalized linear models and introduced the Vanilla ISIS (Van-ISIS) algorithm, allowing selected predictors to be screened out in upcoming iterations. The success of SIS depends on its sure screening property, which was obtained by Fan and Lv (2008) under the marginal correlation assumption. However, despite wide applications of ISIS and Van-ISIS in various scientific fields, their sure screening properties have not been proved during the past decade. To fill this gap, we prove the sure screening properties of three different types of iterative algorithms for linear models without relying on the marginal correlation assumption, where ISIS and Van-ISIS can be regarded as two special cases of them.
- Open Access
Multi-sense Definition Modeling using Word Sense Decompositions
Word embeddings capture syntactic and semantic information about words. Definition modeling aims to make the semantic content in each embedding explicit, by outputting a natural language definition based on the embedding. However, existing definition models are limited in their ability to generate accurate definitions for different senses of the same word. In this paper, we introduce a new method that enables definition modeling for multiple senses. We show how a Gumble-Softmax approach outperforms baselines at matching sense-specific embeddings to definitions during training. In experiments, our multi-sense definition model improves recall over a state-of-the-art single-sense definition model by a factor of three, without harming precision.
Open AccessMulti-sense Definition Modeling using Word Sense Decompositions
Word embeddings capture syntactic and semantic information about words. Definition modeling aims to make the semantic content in each embedding explicit, by outputting a natural language definition based on the embedding. However, existing definition models are limited in their ability to generate accurate definitions for different senses of the same word. In this paper, we introduce a new method that enables definition modeling for multiple senses. We show how a Gumble-Softmax approach outperforms baselines at matching sense-specific embeddings to definitions during training. In experiments, our multi-sense definition model improves recall over a state-of-the-art single-sense definition model by a factor of three, without harming precision.
- 2 August 2019
- Published by Cambridge University Press (CUP) in Political Analysis
- Vol. 28 (1) , 65-86
Ecological Regression with Partial Identification
Ecological inference (EI) is the process of learning about individual behavior from aggregate data. We relax assumptions by allowing for “linear contextual effects,” which previous works have regarded as plausible but avoided due to nonidentification, a problem we sidestep by deriving bounds instead of point estimates. In this way, we offer a conceptual framework to improve on the Duncan–Davis bound, derived more than 65 years ago. To study the effectiveness of our approach, we collect and analyze 8,430 $2\times 2$ EI datasets with known ground truth from several sources—thus bringing considerably more data to bear on the problem than the existing dozen or so datasets available in the literature for evaluating EI estimators. For the 88% of real data sets in our collection that fit a proposed rule, our approach reduces the width of the Duncan–Davis bound, on average, by about 44%, while still capturing the true district-level parameter about 99% of the time. The remaining 12% revert to the Duncan–Davis bound.- 2 August 2019
- Published by Cambridge University Press (CUP) in Political Analysis
- Vol. 28 (1) , 65-86
Ecological Regression with Partial Identification
Ecological inference (EI) is the process of learning about individual behavior from aggregate data. We relax assumptions by allowing for “linear contextual effects,” which previous works have regarded as plausible but avoided due to nonidentification, a problem we sidestep by deriving bounds instead of point estimates. In this way, we offer a conceptual framework to improve on the Duncan–Davis bound, derived more than 65 years ago. To study the effectiveness of our approach, we collect and analyze 8,430 $2\times 2$ EI datasets with known ground truth from several sources—thus bringing considerably more data to bear on the problem than the existing dozen or so datasets available in the literature for evaluating EI estimators. For the 88% of real data sets in our collection that fit a proposed rule, our approach reduces the width of the Duncan–Davis bound, on average, by about 44%, while still capturing the true district-level parameter about 99% of the time. The remaining 12% revert to the Duncan–Davis bound.- 1 January 2019
- Published by International Press of Boston in Statistics and Its Interface
- Vol. 12 (2) , 193-202
An integrative classification model for multiple sclerosis lesion detection in multimodal MRI
International Press of Boston - publishers of scholarly mathematical and scientific journals and books
- 1 January 2019
- Published by International Press of Boston in Statistics and Its Interface
- Vol. 12 (2) , 193-202
An integrative classification model for multiple sclerosis lesion detection in multimodal MRI
International Press of Boston - publishers of scholarly mathematical and scientific journals and books
- Open Access
Ecological Regression with Partial Identification
Ecological inference (EI) is the process of learning about individual behavior from aggregate data. We study a partially identified linear contextual effects model for EI and describe how to estimate the district level parameter averaging over many precincts in the presence of the non-identified parameter of the contextual effect. This may be regarded as a first attempt in this venerable literature to limit the scope of the key form of non-identifiability in EI. To study the operating characteristics of our model, we have amassed the largest collection of data with known ground truth ever applied to evaluate solutions to the EI problem. We collect and study 459 datasets from a variety of fields including public health, political science, and sociology. The datasets contain a total of 2,370,854 geographic units (e.g., precincts), with an average of 5,165 geographic units per dataset. Our replication data are publicly available via the Harvard Dataverse (Jiang et al. 2018) and may serve as a useful resource for future researchers. For all real data sets in our collection that fit our proposed rules, our approach reduces the width of the Duncan and Davis (1953) deterministic bound, on average, by about 45\%, while still capturing the true district level parameter in excess of 97\% of the time. .
Open AccessEcological Regression with Partial Identification
Ecological inference (EI) is the process of learning about individual behavior from aggregate data. We study a partially identified linear contextual effects model for EI and describe how to estimate the district level parameter averaging over many precincts in the presence of the non-identified parameter of the contextual effect. This may be regarded as a first attempt in this venerable literature to limit the scope of the key form of non-identifiability in EI. To study the operating characteristics of our model, we have amassed the largest collection of data with known ground truth ever applied to evaluate solutions to the EI problem. We collect and study 459 datasets from a variety of fields including public health, political science, and sociology. The datasets contain a total of 2,370,854 geographic units (e.g., precincts), with an average of 5,165 geographic units per dataset. Our replication data are publicly available via the Harvard Dataverse (Jiang et al. 2018) and may serve as a useful resource for future researchers. For all real data sets in our collection that fit our proposed rules, our approach reduces the width of the Duncan and Davis (1953) deterministic bound, on average, by about 45\%, while still capturing the true district level parameter in excess of 97\% of the time. .
- Open Access
On Bayesian Oracle Properties
When model uncertainty is handled by Bayesian model averaging (BMA) or Bayesian model selection (BMS), the posterior distribution possesses a desirable "oracle property" for parametric inference, if for large enough data it is nearly as good as the oracle posterior, obtained by assuming unrealistically that the true model is known and only the true model is used. We study the oracle properties in a very general context of quasi-posterior, which can accommodate non-regular models with cubic root asymptotics and partial identification. Our approach for proving the oracle properties is based on a unified treatment that bounds the posterior probability of model mis-selection. This theoretical framework can be of interest to Bayesian statisticians who would like to theoretically justify their new model selection or model averaging methods in addition to empirical results. Furthermore, for non-regular models, we obtain nontrivial conclusions on the choice of prior penalty on model complexity, the temperature parameter of the quasi-posterior, and the advantage of BMA over BMS.
Open AccessOn Bayesian Oracle Properties
When model uncertainty is handled by Bayesian model averaging (BMA) or Bayesian model selection (BMS), the posterior distribution possesses a desirable "oracle property" for parametric inference, if for large enough data it is nearly as good as the oracle posterior, obtained by assuming unrealistically that the true model is known and only the true model is used. We study the oracle properties in a very general context of quasi-posterior, which can accommodate non-regular models with cubic root asymptotics and partial identification. Our approach for proving the oracle properties is based on a unified treatment that bounds the posterior probability of model mis-selection. This theoretical framework can be of interest to Bayesian statisticians who would like to theoretically justify their new model selection or model averaging methods in addition to empirical results. Furthermore, for non-regular models, we obtain nontrivial conclusions on the choice of prior penalty on model complexity, the temperature parameter of the quasi-posterior, and the advantage of BMA over BMS.