RCBDs with a control

The randomized complete block designs, RCBDs, are among the most popular of block designs for comparing a set of experimental treatments. The question of this design's effectiveness when one of the treatments is a control is examined here. Optimality ranges are established for the RBCD in terms of the strength of interest in control comparisons. It is found that if the control treatment is of secondary interest, the RCBD, when not best, is typically near best. This is not so when comparisons with the control are of greater interest than those among the other treatments.     

The application of stochastic approximation methods to the bio-assay problem

A new general model for the bio-assay problem is introduced. It is shown that when the slope of the dose-response curve and the median effective dose is known, the Robbins-Monro method yields an asymptotically optimal estimation procedure. Adaptive procedures are discussed for the case of unknown slope. Results of Monte Carlo studies are given.     

Cointegrated systems I

This paper summarizes from an econometric perspective recent work by statisticians on adaptive estimation. It also presents new findings concerning the adaptive estimability of non-linear regression models.     

Selecting more reliable weibull populations than a control

This paper deals with the selection of Weibull populations that are more reliable than a control population at some specified time. In the case when the shape parameters are known, a locally optimal selection rule is derived. From this rule, a modified one is proposed for the case when the shape parameter is unknown but has a known prior distribution. Simulation study shows that this modified rule is quite robust.     

Nonparametric (smoothed) likelihood and integral equations

We show that there is an intimate connection between the theory of nonparametric (smoothed) maximum likelihood estimators for certain inverse problems and integral equations. This is illustrated by estimators for interval censoring and deconvolution problems. We also discuss the asymptotic efficiency of the MLE for smooth functionals in these models.     

Jump Regressions

We develop econometric tools for studying jump dependence of two processes from high‐frequency observations on a fixed time interval. In this context, only segments of data around a few outlying observations are informative for the inference. We derive an asymptotically valid test for stability of a linear jump relation over regions of the jump size domain. The test has power against general forms of nonlinearity in the jump dependence as well as temporal instabilities. We further propose an efficient estimator for the linear jump regression model that is formed by optimally weighting the detected jumps with weights based on the diffusive volatility around the jump times. We derive the asymptotic limit of the estimator, a semiparametric lower efficiency bound for the linear jump regression, and show that our estimator attains the latter. The analysis covers both deterministic and random jump arrivals. In an empirical application, we use the developed inference techniques to test the temporal stability of market jump betas.     

Idle No More,Facebook and Diffusion

The indigenous-led movement Idle No More leapt into public view during the winter of 2012–2013, bringing round-dance flash-mobs and blockades to communities across Canada and internationally, becoming a symbol of twenty-first century indigenous resistance. This profile examines the first two months of the movement by analysing the structure of social networks being mobilized, by breaking down the Facebook architecture and interface into its various elements, and by examining how these influence diffusion and its constituent sub-processes. It argues that the combination of dense clusters and weak ties in indigenous social networks leveraged the way Facebook facilitated the movement.     

不同产出缺口界定与估算的新方法：一个结构性表述

文章采用结构方程方法,通过构建动态随机一般均衡模型对效率产出缺口、自然律产出缺口以及基于劳动力市场的GGL产出缺口进行了估算,并在此基础上比较了基于结构方程方法估算的产出缺口与基于单变量或单方程方法估算的产出缺口。研究发现,由于结构方程方法完整的反映出变量之间相互作用的机制,进而使得对于产出缺口的估算而言,结构方程方法可能优于单变量或单方程方法。     

$${\mathcal{L}}_p$$ loss functions: a robust bayesian approach

In bayesian inference, the Bayes estimator is the alternative with the minimum expected loss. In most cases, the loss function shows the distance between the alternative and the parameter. Therefore, any distance can lead to a loss function. Among the best known distance functions is L _p one, where the choice of value p may be difficult and arbitrary. This paper examines robust models where the loss function is modelled by family L _p . Our solution concept is the non-dominated alternative. We characterize the non-dominated set by having the posterior distribution function satisfy a particular asymmetry property. We also include an example to illustrate the methodology described.     

The Efficient Methods to Construct Two-Per-Block Design in 2 k Plans

There are many situations which require the performing of two points in one block. Two subjects, in which all effects are available to estimate free of blocks and all main effects and two-factor interactions are estimated free of blocks, are worth to pursue in the 2 ^k design. In this article, we precisely present the minimum number of necessary blocks that enable to estimate the required effects. We also give a methodology to construct the combined designs without using computer search.