Simulation evaluation of small samples based on grey estimation and improved bootstrap

PurposeTo solve the problem of simulation evaluation with small samples, a fresh approach of grey estimation is presented based on classical statistical theory and grey system theory. The purpose of this paper is to make full use of the difference of data distribution and avoid the marginal data being ignored.Design/methodology/approachBased upon the grey distribution characteristics of small sample data, the definition about a new concept of grey relational similarity measure comes into being. At the same time, the concept of sample weight is proposed according to the grey relational similarity measure. Based on the new definition of grey weight, the grey point estimation and grey confidence interval are studied. Then the improved Bootstrap resampling is designed by uniform distribution and randomness as an important supplement of the grey estimation. In addition, the accuracy of grey bilateral and unilateral confidence intervals is introduced by using the new grey relational similarity measure approach.FindingsThe new small sample evaluation method can realize the effective expansion and enrichment of data and avoid the excessive concentration of data. This method is an organic fusion of grey estimation and improved Bootstrap method. Several examples are used to demonstrate the feasibility and validity of the proposed methods to illustrate the credibility of some simulation data, which has no need to know the probability distribution of small samples.Originality/valueThis research has completed the combination of grey estimation and improved Bootstrap, which makes more reasonable use of the value of different data than the unimproved method.

Encoding Diagnostic Codes for Privacy-Preserving Record Linkage

IntroductionDiagnostic codes, such as the ICD-10, may be considered as sensitive information. If such codes have to be encoded using current methods for data linkage, all hierarchical information given by the code positions will be lost. We present a technique (HPBFs) for preserving the hierarchical information of the codes while protecting privacy. The new method modifies a widely used Privacy-preserving Record Linkage (PPRL) technique based on Bloom filters for the use with hierarchical codes. Objectives and ApproachAssessing the similarities of hierarchical codes requires considering the code positions of two codes in a given diagnostic hierarchy. The hierarchical similarities of the original diagnostic code pairs should correspond closely to the similarity of the encoded pairs of the same code. Furthermore, to assess the hierarchy-preserving properties of an encoding, the impact on similarity measures from differing code positions at all levels of the code hierarchy can be evaluated. A full match of codes should yield a higher similarity than partial matches. Finally, the new method is tested against ad-hoc solutions as an addition to a standard PPRL setup. This is done using real-world mortality data with a known link status of two databases. ResultsIn all applications for encoded ICD codes where either categorical discrimination, relational similarity or linkage quality in a PPRL setting is required, HPBFs outperform other known methods. Lower mean differences and smaller confidence intervals between clear-text codes and encrypted code pairs were observed, indicating better preservation of hierarchical similarities. Finally, using these techniques allows for much better hierarchical discrimination for partial matches. ConclusionThe new technique yields better linkage results than all other known methods to encrypt hierarchical codes. In all tests, comparing categorical discrimination, relational similarity and PPRL linkage quality, HPBFs outperformed methods currently used.

The Internal Structure of Semantic Relations: Effects of Relational Similarity and Typicality

We examined whether the processing of semantic relations shows typicality effects similar to those found for the processing of entity concepts. Participants performed four relational processing tasks with the same set of word-pair stimuli: relational exemplar generation; similarity ranking; analogical verification; and a paired-associate learning task. In the similarity ranking task, we gathered separate rankings for relational, role and semantic similarity between word pairs. We found significant correlations at the item level among relational generation frequencies, analogical verification RTs/accuracy and relational luring in associative memory. Relational similarity predicted exemplar generation frequencies, analogical verification RTs/accuracy, and relational luring in associative memory. Role similarity predicted exemplar generation frequency, and analogical verification RTs, but not relational luring. Semantic similarity did not predict any of the tasks, after controlling for the other two factors. Contrary to current theories which posit that semantic similarity is more important for retrieving relevant analogues, and that analogical mapping is based on role-filler bindings, relational similarity was the strongest predictor across all tasks. These results suggest that just like entity concepts, semantic relations have an internal structure that gives rise to typicality effects across a variety of tasks, which could provide constraints for testing competing theories of relational representation.

Similarity Judgments Predict N400 Amplitude Differences between Taxonomic Category Members and Thematic Associates

Human similarity judgments do not reliably conform to the predictions of leading theories of psychological similarity. Evidence from the triad similarity judgment task shows that people often identify thematic associates like DOG and BONE as more similar than taxonomic category members like DOG and CAT, even though thematic associates lack the type of featural or relational similarity that is foundational to theories of psychological similarity. This specific failure to predict human behavior has been addressed as a consequence of education and other individual differences, an artifact of the triad similarity judgment paradigm, or a shortcoming in psychological accounts of similarity. We investigated the judged similarity of semantically-related concepts (taxonomic category members and thematic associates) as it relates to other task-independent measures of semantic knowledge and access. Participants were assessed on reading and language ability, then event-related potentials (ERPs) were collected during a passive, sequential word reading task that presented pseudowords and taxonomically-related, thematically-related, and unrelated word sequences, and, finally, similarity judgments were collected with the classic two-alternative forced-choice triad task. The results uncovered a correspondence between ERP amplitude and triad-based similarity judgments---similarity judgment behavior reliably predicts ERP amplitude during passive word reading, absent of any instruction to consider similarity. It was also found that individual differences in reading and language ability independently predicted ERP amplitude. This evidence suggests that similarity judgments are driven by reliable patterns of thought that are not solely rooted in the interpretation of task goals or reading and language ability.

Distributed Code for Semantic Relations Predicts Neural Similarity

The ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coded as atomistic links in a semantic network, or as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations remains to be empirically established. The present study combined computational modeling and neuroimaging to investigate the representation and comparison of abstract semantic relations in the brain. By using sequential presentation of verbal analogies, we decoupled the neural activity associated with encoding the representation of the first-order semantic relation between words in a pair from that associated with the second-order comparison of two relations. We tested alternative computational models of relational similarity in order to distinguish between rival accounts of how semantic relations are coded and compared in the brain. Analyses of neural similarity patterns supported the hypothesis that semantic relations are coded, in the parietal cortex, as distributed representations over a pool of abstract relations specified in a theory-based taxonomy. These representations, in turn, provide the immediate inputs to the process of analogical comparison, which draws on a broad frontoparietal network. This study sheds light not only on the form of relation representations but also on their specific content.SignificanceRelations provide basic building blocks for language and thought. For the past half century, cognitive scientists exploring human semantic memory have sought to identify the code for relations. In a neuroimaging paradigm, we tested alternative computational models of relation processing that predict patterns of neural similarity during distinct phases of analogical reasoning. The findings allowed us to draw inferences not only about the form of relation representations, but also about their specific content. The core of these distributed representations is based on a relatively small number of abstract relation types specified in a theory-based taxonomy. This study helps to resolve a longstanding debate concerning the nature of the conceptual and neural code for semantic relations in the mind and brain.