ON BOOTSTRAP HYPOTHESIS TESTING

We describe methods for constructing bootstrap hypothesis tests, illustrating our approach using analysis of variance. The importance of pivotalness is discussed. Pivotal statistics usually result in improved accuracy of level. We note that hypothesis tests and confidence intervals call for different methods of resampling, so as to ensure that accurate critical point estimates are obtained in the former case even when data fail to comply with the null hypothesis. Our main points are illustrated by a simulation study and application to three real data sets.     

多次采样与希尔伯特变换

针对文献［１］提出的多次采样方法，证明了多次采样法在一定条件下等效于一种希尔伯特变换器，并用数据证明：当信号的相对带宽小于８０％时，９次采样所获得的正交分量误差可忽略。     

几种方差概念的比较

当分布数列中各标志值代表的含义不完全一样时，方差仅仅描述总体的离散程度，而半方差概念则能更准确地描述所关注的标志值的离散程度；相对方差反映的是分布数列中各标志值的相对离散程度，与标准差系数一样，相对方差可用于不同性质、不同单位或具有不同水平的数列的离散程度的比较，而方差却不适合用于这种比较；半相对方差是综合了半方差和相对方差优点的一个新指标。上述几种方差概念，从不同视角描述了数列的离散程度，各有优劣点，而且相互间有着紧密的联系。     

A Spatial-temporal Model for Temperature with Seasonal Variance

We propose a spatial-temporal stochastic model for daily average surface temperature data. First, we build a model for a single spatial location, independently on the spatial information. The model includes trend, seasonality, and mean reversion, together with a seasonally dependent variance of the residuals. The spatial dependency is modelled by a Gaussian random field. Empirical fitting to data collected in 16 measurement stations in Lithuania over more than 40 years shows that our model captures the seasonality in the autocorrelation of the squared residuals, a property of temperature data already observed by other authors. We demonstrate through examples that our spatial-temporal model is applicable for prediction and classification.     

Clustering and combinatorial optimization in recursive supervised learning

The use of combinations of weak learners to learn a dataset has been shown to be better than the use of a single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be the best off the shelf classifier. However, some problems still exist, including determining the optimal number of weak learners and the over fitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of global search, weak learning and pattern distribution. In this chapter, we revise the global search component by replacing it with a cluster based combinatorial optimization. Patterns are clustered according to the output space of the problem, i.e., natural clusters are formed based on patterns belonging to each class. A combinatorial optimization problem is therefore created, which is solved using evolutionary algorithms. The evolutionary algorithms identify the “easy” and the “difficult” clusters in the system. The removal of the easy patterns then gives way to the focused learning of the more complicated patterns. The problem therefore becomes recursively simpler. Over fitting is overcome by using a set of validation patterns along with a pattern distributor. An algorithm is also proposed to use the pattern distributor to determine the optimal number of recursions and hence the optimal number of weak learners for the problem. Empirical studies show generally good performance when compared to other state of the art methods.     

On the trimmed mean and minimax-variance L-estimation in Kolmogorov neighbourhoods

We consider the properties of the trimmed mean, as regards minimax-variance L-estimation of a location parameter in a Kolmogorov neighbourhood K() of the normal distribution: We first review some results on the search for an L-minimax estimator in this neighbourhood, i.e. a linear combination of order statistics whose maximum variance in K_t() is a minimum in the class of L-estimators. The natural candidate – the L-estimate which is efficient for that member of K_t,() with minimum Fisher information – is known not to be a saddlepoint solution to the minimax problem. We show here that it is not a solution at all. We do this by showing that a smaller maximum variance is attained by an appropriately trimmed mean. We argue that this trimmed mean, as well as being computationally simple – much simpler than the efficient L-estimate referred to above, and simpler than the minimax M- and R-estimators – is at least “nearly” minimax.     

中国能源消耗减物质化分析

1980-2004年中国能源消耗(生活消耗除外)出现了较大波动,本文运用完全分解模型研究了能源消耗的减物质化过程。结果表明,1980-2004年累计节能量为80790万吨标准煤,同时受国家宏观调控政策的影响,1980-2001年中国能源消耗减物质化处于波动之中,节能缺口幅度大致在1.0之内频繁波动(绝对值),2001年以后中国能源消耗减物质化出现了剧变,节能缺口急剧下降,实际节能率远远低于潜在节能率,能源供应出现了紧张局面。     

Inference for general parametric functions in box‐Cox‐type transformation models

The authors propose a simple but general method of inference for a parametric function of the Box‐Cox‐type transformation model. Their approach is built upon the classical normal theory but takes parameter estimation into account. It quickly leads to test statistics and confidence intervals for a linear combination of scaled or unsealed regression coefficients, as well as for the survivor function and marginal effects on the median or other quantité functions of an original response. The authors show through simulations that the finite‐sample performance of their method is often superior to the delta method, and that their approach is robust to mild departures from normality of error distributions. They illustrate their approach with a numerical example.     

李金发与象征主义之滥觞

李金发的诗歌创作实现了民族文化与西方象征主义的融合,其作品最初被作为解构旧文学思想的工具,虽然遭到主流文学中部分文人的攻讦,但终究还是被作为象征主义代表纳入新文学体秩序之中.李金发的变异象征主义本是蕴涵着反现代化的现代性,然而在1930年代至1940年代不断变化着的社会文化和外部局势主导下,最终也不得不以牺牲自己所追求的艺术生命而依附于革命化和现代化,从而销匿于新的文学秩序之中.     

An alternative estimator for multiple characteristics in PPS sampling

The problem considered relates to large-scale sample surveys. A new estimator of population total for the characteristics that are poorly correlated with the selection probabilities has been developed for the PPSWR sampling scheme. The relative efficiency of the proposed estimator has been studied under a super-population model. A numerical investigation into the performance of the estimator has also been made.