论语言符号认知中的类比机制

类比是一种创新性的符号演算过程,它存在于归纳、演绎和不明推论三种逻辑推理过程中．主要类比对象的特征和关系系统。类比过程具有系统映射性、方向性和推理创新性,它是语言符号交际和理解的机制．是语言发展的动力,是语言理论建构的方法,也是人类认知行为的基本方式。     

Bayesian Causality

Abstract

Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework. In this approach, causality statements are viewed as hypotheses, or models, about the world and the fundamental object to be computed is the posterior distribution of the causal hypotheses, given the data and the background knowledge. Computation of the posterior, illustrated here in simple examples, may involve complex probabilistic modeling but this is no different than in any other Bayesian modeling situation. The main advantage of the approach is its connection to the axiomatic foundations of the Bayesian framework, and the general uniformity with which it can be applied to a variety of causality settings, ranging from specific to general cases, or from causes of effects to effects of causes.     

Bootstrap-based inferential improvements in beta autoregressive moving average model

We consider the issue of performing accurate small sample inference in beta autoregressive moving average model, which is useful for modeling and forecasting continuous variables that assume values in the interval (0,?1). The inferences based on conditional maximum likelihood estimation have good asymptotic properties, but their performances in small samples may be poor. This way, we propose bootstrap bias corrections of the point estimators and different bootstrap strategies for confidence interval improvements. Our Monte Carlo simulations show that finite sample inference based on bootstrap corrections is much more reliable than the usual inferences. We also presented an empirical application.     

The free encyclopedia that anyone can dispute: An analysis of the micro-structural dynamics of positive and negative relations in the production of contentious Wikipedia articles

We consider two rival hypotheses on the emergence of organization in open production communities. According to the first (“reputation hypothesis”), patterns of agreement and disagreement among participants in open production communities are explained by differences in individual reputation for quality of contribution. The reputation hypothesis predicts that participants will tend to agree with more reputable others and disagree with less reputable others thus contributing to establish a stable open production community. According to the second hypothesis (“balance hypothesis”), patterns of agreement and disagreement are explained by membership in sub-communities of “friends” and “enemies.” The balance hypothesis predicts that participants in open production communities will agree mainly with friends and disagree mainly with enemies, regardless of considerations about reputation for the quality of their contributions. In this paper, we examine which one of these hypotheses is more consistent with patterns of positive and negative interaction events observed during the production of the complete set of 1,206 English-language Wikipedia articles officially considered controversial. We specify and estimate new models for signed and weighted relational event networks predicting the probability that a user deletes the contributions of another user – thus expressing personal disagreement – and/or protects the contributions of another user against deletion from third parties – thus expressing personal agreement. In an analysis of positive and negative interaction among Wikipedia contributors consisting of more than 60 million observations, we find strong support for the balance hypothesis and for the predictions of the reputation hypothesis that are more consistent with alter-centric interpretations of social status as conferred by alters through observable acts of deference.     

Automated selection of post‐strata using a model‐assisted regression tree estimator

Despite having desirable properties, model‐assisted estimators are rarely used in anything but their simplest form to produce official statistics. This is due to the fact that the more complicated models are often ill suited to the available auxiliary data. Under a model‐assisted framework, we propose a regression tree estimator for a finite‐population total. Regression tree models are adept at handling the type of auxiliary data usually available in the sampling frame and provide a model that is easy to explain and justify. The estimator can be viewed as a post‐stratification estimator where the post‐strata are automatically selected by the recursive partitioning algorithm of the regression tree. We establish consistency of the regression tree estimator and a variance estimator, along with asymptotic normality of the regression tree estimator. We compare the performance of our estimator to other survey estimators using the United States Bureau of Labor Statistics Occupational Employment Statistics Survey data.     

A fiducial p-value approach for comparing heteroscedastic regression models

To study the equality of regression coefficients in several heteroscedastic regression models, we propose a fiducial-based test, and theoretically examine the frequency property of the proposed test. We numerically compare the performance of the proposed approach with the parametric bootstrap (PB) approach. Simulation results indicate that the fiducial approach controls the Type I error rates satisfactorily regardless of the number of regression models and sample sizes, whereas the PB approach tends to be a little of liberal in some scenarios. Finally, the proposed approach is applied to an analysis of a real dataset for illustration.     

Generalized fiducial inference for generalized exponential distribution

This article mainly considers interval estimation of the scale and shape parameters of the generalized exponential (GE) distribution. We adopt the generalized fiducial method to construct a kind of new confidence intervals for the parameters of interest and compare them with the frequentist and Bayesian methods. In addition, we give the comparison of the point estimation based on the frequentist, generalized fiducial and Bayesian methods. Simulation results show that a new procedure based on generalized fiducial inference is more applicable than the non-fiducial methods for the point and interval estimation of the GE distribution. Finally, two lifetime data sets are used to illustrate the application of our new procedure.     

Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions

For interval estimation of a proportion, coverage probabilities tend to be too large for “exact” confidence intervals based on inverting the binomial test and too small for the interval based on inverting the Wald large-sample normal test (i.e., sample proportion ± z-score × estimated standard error). Wilson's suggestion of inverting the related score test with null rather than estimated standard error yields coverage probabilities close to nominal confidence levels, even for very small sample sizes. The 95% score interval has similar behavior as the adjusted Wald interval obtained after adding two “successes” and two “failures” to the sample. In elementary courses, with the score and adjusted Wald methods it is unnecessary to provide students with awkward sample size guidelines.     

Objective priors for causal AR(p) with partial autocorrelations

We consider the problem of deriving formal objective priors for the causal/stationary autoregressive model of order p. We compare the frequentist behaviour of the most common default priors, namely the uniform (over the stationarity region) prior, the Jeffreys’ prior and the reference prior.     

The Two-Scale Plot: An Exploratory Display of Data with Heterogeneous Variances

The history of the analysis of unbalanced factorial designs is traced from Yates's original papers (Yates 1933, 1934) to the beginning of the computational revolution in the 1960s. Emphasis is placed on putting the methods proposed during this period in perspective in view of our present understanding.