Bayesian empirical likelihood for quantile regression

Bayesian inference provides a flexible way of combining data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian inference for quantile regression demands careful investigation. This paper considers the Bayesian empirical likelihood approach to quantile regression. Taking the empirical likelihood into a Bayesian framework, we show that the resultant posterior from any fixed prior is asymptotically normal; its mean shrinks toward the true parameter values, and its variance approaches that of the maximum empirical likelihood estimator. A more interesting case can be made for the Bayesian empirical likelihood when informative priors are used to explore commonality across quantiles. Regression quantiles that are computed separately at each percentile level tend to be highly variable in the data sparse areas (e.g., high or low percentile levels). Through empirical likelihood, the proposed method enables us to explore various forms of commonality across quantiles for efficiency gains. By using an MCMC algorithm in the computation, we avoid the daunting task of directly maximizing empirical likelihood. The finite sample performance of the proposed method is investigated empirically, where substantial efficiency gains are demonstrated with informative priors on common features across several percentile levels. A theoretical framework of shrinking priors is used in the paper to better understand the power of the proposed method.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1005 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135059/1/insr12114.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135059/2/insr12114_am.pd

Rejoinder

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134939/1/insr12181.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134939/2/insr12181_am.pd

Credit Risk Assessment of Banks' Loan Enterprise Customer Based on State-Constraint

Commercial banks are facing increasingly complex enterprise loan customers and businesses. It is important for banks' enterprise loan business to efficiently assess credit risks. Our study builds an enterprise credit risk assessment model based on the state and constraint of bank and customer, and get empirical researches with RF, SVM and DT algorithms. The results show that our model has excellent performance with accuracy 99 % and great characteristic importance in the evaluation of enterprise credit risk. The study can provide important decision-making reference for bank loan business and enrich the theoretical system of bank credit risk research

Efficacy of Zhenjingdingzhi decoction in treating insomnia with Qi-deficiency of heart and gallbladder: a randomized, double-blind, controlled trial

AbstractObjectiveTo evaluate the clinical efficacy of Zhenjingdingzhi decoction in treating insomnia with Qi-deficiency of heart and gallbladder.MethodsWe conducted a double-blind, randomized, controlled trial involving 100 patients with insomnia of Qi-deficiency of heart and gallbladder. Patients were randomly divided into the treatment group (n = 50) and the control group (n = 50) according to a random number table. The treatment group was given Zhenjingdingzhi decoction, while the control group was treated with Suanzaoren decoction. the pharmacological treatment lasted for 8 weeks. The clinical efficacy was assessed by using Spiegel scale, Pittsburgh sleep quality index (PSQI) and Traditional Chinese Medicine (TCM) syndrome scores.ResultsComparing Spiegel scores between the two groups at 4 and 8 weeks, the differences in curative effect between the two groups were both significant (both P < 0.05). The total effective rate was 46% in the treatment group and 27.7% in the control group at 4 weeks, and 80% and 53.2% at 8 weeks, respectively; After 8 weeks, PSQI scores showed that the total effective rates differed significantly between the two groups (P < 0.01): 84% in the treatment group and 59.6% in the control group; In improving sleep quality and sleep duration, the curative effect of the treatment group was better than that of the control group (P < 0.05). TCM syndrome, especially insomnia and palpitation, was improved better in the treatment group after 8 weeks as compared to that in the control group (P < 0.05). The total effective rate of the two groups was 84% and 66%, respectively.ConclusionZhenjingdingzhi decoction is effective and safe for the treatment of insomnia with Qi-deficiency of heart and gallbladder, especially for improving sleep quality and sleep duration

Considerable MHC Diversity Suggests That the Functional Extinction of Baiji Is Not Related to Population Genetic Collapse

To further extend our understanding of the mechanism causing the current nearly extinct status of the baiji (Lipotes vexillifer), one of the most critically endangered species in the world, genetic diversity at the major histocompatibility complex (MHC) class II DRB locus was investigated in the baiji. Nine highly divergent DRB alleles were identified in 17 samples, with an average of 28.4 (13.2%) nucleotide difference and 16.7 (23.5%) amino acid difference between alleles. The unexpectedly high levels of DRB allelic diversity in the baiji may partly be attributable to its evolutionary adaptations to the freshwater environment which is regarded to have a higher parasite diversity compared to the marine environment. In addition, balancing selection was found to be the main mechanisms in generating sequence diversity at baiji DRB gene. Considerable sequence variation at the adaptive MHC genes despite of significant loss of neutral genetic variation in baiji genome might suggest that intense selection has overpowered random genetic drift as the main evolutionary forces, which further suggested that the critically endangered or nearly extinct status of the baiji is not an outcome of genetic collapse

Grounded Image Text Matching with Mismatched Relation Reasoning

This paper introduces Grounded Image Text Matching with Mismatched Relation (GITM-MR), a novel visual-linguistic joint task that evaluates the relation understanding capabilities of transformer-based pre-trained models. GITM-MR requires a model to first determine if an expression describes an image, then localize referred objects or ground the mismatched parts of the text. We provide a benchmark for evaluating pre-trained models on this task, with a focus on the challenging settings of limited data and out-of-distribution sentence lengths. Our evaluation demonstrates that pre-trained models lack data efficiency and length generalization ability. To address this, we propose the Relation-sensitive Correspondence Reasoning Network (RCRN), which incorporates relation-aware reasoning via bi-directional message propagation guided by language structure. RCRN can be interpreted as a modular program and delivers strong performance in both length generalization and data efficiency

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

How to identify semantic relations among entities in a document when only a few labeled documents are available? Few-shot document-level relation extraction (FSDLRE) is crucial for addressing the pervasive data scarcity problem in real-world scenarios. Metric-based meta-learning is an effective framework widely adopted for FSDLRE, which constructs class prototypes for classification. However, existing works often struggle to obtain class prototypes with accurate relational semantics: 1) To build prototype for a target relation type, they aggregate the representations of all entity pairs holding that relation, while these entity pairs may also hold other relations, thus disturbing the prototype. 2) They use a set of generic NOTA (none-of-the-above) prototypes across all tasks, neglecting that the NOTA semantics differs in tasks with different target relation types. In this paper, we propose a relation-aware prototype learning method for FSDLRE to strengthen the relational semantics of prototype representations. By judiciously leveraging the relation descriptions and realistic NOTA instances as guidance, our method effectively refines the relation prototypes and generates task-specific NOTA prototypes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches by average 2.61% $F_1$ across various settings of two FSDLRE benchmarks.Comment: Accepted to EMNLP 202