An alternative to model selection in ordinary regression

The weaknesses of established model selection procedures based on hypothesis testing and similar criteria are discussed and an alternative based on synthetic (composite) estimation is proposed. It is developed for the problem of prediction in ordinary regression and its properties are explored by simulations for the simple regression. Extensions to a general setting are described and an example with multiple regression is analysed. Arguments are presented against using a selected model for any inferences.     

Loss functions for estimation of extrema with an application to disease mapping

It is often of interest to find the maximum or near maxima among a set of vector‐valued parameters in a statistical model; in the case of disease mapping, for example, these correspond to relative‐risk “hotspots” where public‐health intervention may be needed. The general problem is one of estimating nonlinear functions of the ensemble of relative risks, but biased estimates result if posterior means are simply substituted into these nonlinear functions. The authors obtain better estimates of extrema from a new, weighted ranks squared error loss function. The derivation of these Bayes estimators assumes a hidden‐Markov random‐field model for relative risks, and their behaviour is illustrated with real and simulated data.     

Modelling map positional error to infer true feature location

The authors consider the issue of map positional error, or the difference between location as represented in a spatial database (i.e., a map) and the corresponding unobservable true location. They propose a fully model‐based approach that incorporates aspects of the map registration process commonly performed by users of geographic informations systems, including rubber‐sheeting. They explain how estimates of positional error can be obtained, hence estimates of true location. They show that with multiple maps of varying accuracy along with ground truthing data, suitable model averaging offers a strategy for using all of the maps to learn about true location.     

An evaluation of the childhood family structure measures from the sixth wave of the British Household Panel Survey

Summary.  The paper performs an evaluation of the data that were collected in the sixth wave of the British Household Panel Survey (BHPS) on childhood family structure. After comparing such data with a large number of studies by using external sources, we find that the BHPS data overestimate the proportion of people who report an experience of life in a non-intact family during childhood by about 10%. Although an explanation based on recall error that deteriorates with the age of the BHPS respondents is possible, the overestimation is likely to be accounted for by non-ignorable attrition that may affect most of the comparison studies based on longitudinal data. Conversely, comparisons with other independent measurements from the BHPS itself reveal that the wave 6 data underestimate the proportion of young people who have lived at least part of their childhood in a non-intact family by about 8%. The probability of disagreement between these two sets of measures is strongly associated with poor interview characteristics, which may affect the comparison measure more than the wave 6 measure. Despite such differences, there is therefore a substantial degree of similarity between the family structure information that was collected in the sixth wave of the BHPS and the host of highly diverse records against which it has been compared.     

Improving on truncated linear estimates of exponential and gamma scale parameters

We consider the problem of estimating the scale parameter of an exponential or a gamma distribution under squared error loss when the scale parameter θ is known to be greater than some fixed value θ₀. Natural estimators in this setting include truncated linear functions of the sufficient statistic. Such estimators are typically inadmissible, but explicit improvements seem difficult to find. Some are presented here. A particularly interesting finding is that estimators which are admissible in the untruncated problem which take values only in the interior of the truncated parameter space are found to be inadmissible for the truncated problem.     

Efficient Estimation of a Distribution Function under Quadrant Dependence

LetX₁,X₂, ..., be real-valued random variables forming a strictly stationary sequence, and satisfying the basic requirement of being either pairwise positively quadrant dependent or pairwise negatively quadrant dependent. LetF^ be the marginal distribution function of theX_ips, which is estimated by the empirical distribution functionF_n and also by a smooth kernel-type estimateF_n, by means of the segmentX₁, ...,X_n. These estimates are compared on the basis of their mean squared errors (MSE). The main results of this paper are the following. Under certain regularity conditions, the optimal bandwidth (in the MSE sense) is determined, and is found to be the same as that in the independent identically distributed case. It is also shown thatn MSE(F_n(t)) andnMSE (F^_n(t)) tend to the same constant, asn→∞ so that one can not discriminate be tween the two estimates on the basis of the MSE. Next, ifi(n) = min {k∈{1, 2, ...}; MSE (F_k(t)) ≤ MSE (F_n(t))}, then it is proved thati(n)/n tends to 1, asn→∞. Thus, once again, one can not choose one estimate over the other in terms of their asymptotic relative efficiency. If, however, the squared bias ofF^_n(t) tends to 0 sufficiently fast, or equivalently, the bandwidthh_n satisfies the requirement thatnh³_n→ 0, asn→∞, it is shown that, for a suitable choice of the kernel, (i(n) ?n)/(nh_n) tends to a positive number, asn→∞ It follows that the deficiency ofF_n(t) with respect toF^_n(t),i(n) ?n, is substantial, and, actually, tends to ∞, asn→∞. In terms of deficiency, the smooth estimateF^_n(t) is preferable to the empirical distribution functionF_n(t)     

On the shrinkage of local linear curve estimators

Local linear curve estimators are typically constructed using a compactly supported kernel, which minimizes edge effects and (in the case of the Epanechnikov kernel) optimizes asymptotic performance in a mean square sense. The use of compactly supported kernels can produce numerical problems, however. A common remedy is ridging, which may be viewed as shrinkage of the local linear estimator towards the origin. In this paper we propose a general form of shrinkage, and suggest that, in practice, shrinkage be towards a proper curve estimator. For the latter we propose a local linear estimator based on an infinitely supported kernel. This approach is resistant against selection of too large a shrinkage parameter, which can impair performance when shrinkage is towards the origin. It also removes problems of numerical instability resulting from using a compactly supported kernel, and enjoys very good mean squared error properties.     

核电厂人因失误机理研究:QAP分析

为了明确核电厂人因失误机理并为后期人因失误的精准防控提供理论依据,文章采用“组织定向人因失误分析技术”对2010—2020年核电厂208件人因失误事件报告进行分析,获得样本数据,经统计辨识主要的人因失误类型(知识型、规则型和技能型)和影响因素(如教育培训、组织设计、规程、人机界面等),结合影响因素之间的相关性及因子分析构建主要人因失误场景(如知识/技能经验、注意力、压力和态度等)。由于各因素之间存在相互影响,因而采用QAP方法(Quadratic Assignment Procedure,二次指派程序)对人因失误场景进行相关分析和回归分析,建立知识型、规则型、技能型人因失误因果模型,辨识不同的人因失误场景,揭示人因失误机理。结果表明,不同的人因失误类型其产生机理以及场景显著性不同,为人因失误的精准防控提供理论依据。     

中国狭义货币及其各组合分量的需求模型

主要研究流通中现金、活期存款和狭义货币的需求函数．实证结果表明现金、活期存款和狭义货币与收入、价格水平、利率和货币化进程存在长期均衡关系．中国的货币供给增长率大于经济增长和通货膨胀率之和,原因在于中国存在货币化现象,货币存量的增加除了满足经济增长的需要还要满足经济货币化的那一部分     

中小板ETF的价格发现能力研究

使用日内5分钟交易高频数据,通过误差修正模型和方差分解等技术研究中小板ETF与其标的指数间的价格发现,进而探讨信息传递过程。实证结果显示：中小板ETF价格与中小板P指数间存在协整关系,达到了长期均衡;价格发现能力上,中小板P指数领先中小板ETF;中小板P指数受到新信息影响所产生的冲击大于中小板ETF价格所产生的冲击,中小板P指数对预测误差方差的解释能力强于中小板ETF价格,中小板P指数为信息传递的领先指标。我国ETF市场的有效性有待提升。