Projection Pursuit Through Relative Entropy Minimization

Projection Pursuit methodology permits to solve the difficult problem of finding an estimate of a density defined on a set of very large dimension. In his seminal article, “Projection Pursuit”, Huber (1985 Huber , P. ( 1985 ). Projection pursuit . The Annals of Statistics 13 ( 2 ): 435 – 525 With discussion .[Crossref], [Web of Science ®] , [Google Scholar]) evidenced the interest of the Projection Pursuit method thanks to the factorization of a density into a Gaussian component and some residual density in a context of Kullback–Leibler divergence maximisation.

In the present article, we introduce a new algorithm, and in particular, a test for the factorisation of a density estimated from an iid sample.     

论跨文化言语交际中的汉英语用迁移

在跨文化言语交际中,由于汉英语用规则作用于交际活动,导致语用语言趋异现象产生,同时在言语行为实施策略的选择方面出现差异.以汉语为母语的英语学习者,经常使用汉文化的社会语言规则,来理解、解释和评价英语本族语者的言语行为,将汉语语言表达习惯迁移到目的语中,使自己的语用用意被误解或不可理解,造成跨文化交际的失败.     

文化选择与生态危机

生物学与文化人类学相互借用、交替推进,文化人类学从生物学中引进进化论具有不完备性,为了实现生物学与文化人类学理论的兼容,需要在自然选择和人工选择的基础上引进文化选择和文化选择歧化这两个概念以满足生态人类学理论分析的需要,也才能明辨人类社会所面对的生态灾变的文化成因。     

卖空限制、意见分歧与IPO溢价

以2001～2007年的IPO公司为样本,以分析师对IPO上市首日价格预测的离散程度和上市首日换手率衡量投资者之间的意见分歧,研究在严格卖空限制下,意见分歧程度的大小是否影响IPO溢价.研究结果表明,分析师对上市首日价格预测的离散程度越大,上市首日换手率越高,IPO溢价程度越高;分析师预测的乐观极限越高,IPO溢价程度越高.     

Parameter estimation procedures for exponential-family random graph models on count-valued networks: A comparative simulation study

The exponential-family random graph models (ERGMs) have emerged as an important framework for modeling social networks for a wide variety of relational types. ERGMs for valued networks are less well-developed than their unvalued counterparts, and pose particular computational challenges. Network data with edge values on the non-negative integers (count-valued networks) is an important such case, with examples ranging from the magnitude of migration and trade flows between places to the frequency of interactions and encounters between individuals. Here, we propose an efficient parallelizable subsampled maximum pseudo-likelihood estimation (MPLE) scheme for count-valued ERGMs, and compare its performance with existing Contrastive Divergence (CD) and Monte Carlo Maximum Likelihood Estimation (MCMLE) approaches via a simulation study based on migration flow networks in two U.S. states. Our results suggest that edge value variance is a key factor in method performance, while network size mainly influences their relative merits in computational time. For small-variance networks, all methods perform well in point estimations while CD greatly overestimates uncertainties, and MPLE underestimates them for dependence terms; all methods have fast estimation for small networks, but CD and subsampled multi-core MPLE provides speed advantages as network size increases. For large-variance networks, both MPLE and MCMLE offer high-quality estimates of coefficients and their uncertainty, but MPLE is significantly faster than MCMLE; MPLE is also a better seeding method for MCMLE than CD, as the latter makes MCMLE more prone to convergence failure. The study suggests that MCMLE and MPLE should be the default approach to estimate ERGMs for small-variance and large-variance valued networks, respectively. We also offer further suggestions regarding choice of computational method for valued ERGMs based on data structure, available computational resources and analytical goals.     

Divergence versus decision P-values: A distinction worth making in theory and keeping in practice: Or,how divergence P-values measure evidence even when decision P-values do not

There are two distinct definitions of “P-value” for evaluating a proposed hypothesis or model for the process generating an observed dataset. The original definition starts with a measure of the divergence of the dataset from what was expected under the model, such as a sum of squares or a deviance statistic. A P-value is then the ordinal location of the measure in a reference distribution computed from the model and the data, and is treated as a unit-scaled index of compatibility between the data and the model. In the other definition, a P-value is a random variable on the unit interval whose realizations can be compared to a cutoff α to generate a decision rule with known error rates under the model and specific alternatives. It is commonly assumed that realizations of such decision P-values always correspond to divergence P-values. But this need not be so: Decision P-values can violate intuitive single-sample coherence criteria where divergence P-values do not. It is thus argued that divergence and decision P-values should be carefully distinguished in teaching, and that divergence P-values are the relevant choice when the analysis goal is to summarize evidence rather than implement a decision rule.