Using the Fraction of Missing Information to Identify Auxiliary Variables for Imputation Procedures via Proxy Pattern‐mixture Models

In many surveys, imputation procedures are used to account for non‐response bias induced by either unit non‐response or item non‐response. Such procedures are optimised (in terms of reducing non‐response bias) when the models include covariates that are highly predictive of both response and outcome variables. To achieve this, we propose a method for selecting sets of covariates used in regression imputation models or to determine imputation cells for one or more outcome variables, using the fraction of missing information (FMI) as obtained via a proxy pattern‐mixture (PMM) model as the key metric. In our variable selection approach, we use the PPM model to obtain a maximum likelihood estimate of the FMI for separate sets of candidate imputation models and look for the point at which changes in the FMI level off and further auxiliary variables do not improve the imputation model. We illustrate our proposed approach using empirical data from the Ohio Medicaid Assessment Survey and from the Service Annual Survey.     

Logistic回归的ArctanLASSO惩罚似然估计及应用

Logistic回归是计量经济学中应用最广的离散选择模型。当变量个数较多时,极大似然估计解释性较差,为此本文基于新的惩罚函数ArctanLASSO,给出Logistic回归的一种非凸惩罚似然估计进行参数估计和变量选取,并证明了估计量的n1/2相合性和Oracle性质。本文结合二阶近似处理、LLA方法和梯度下降法给出估计算法,并通过最小化BIC准则对正则化参数进行选取。模拟数据分析显示,当样本量较大时,该方法在参数估计和变量选取两个方面都优于传统的LASSO、SCAD和MCP方法,样本量较小时,该方法同样具有很大优势。实际数据分析表明,该方法很好地权衡了拟合程度和非零系数的选择,是最优的备选模型,具有重要的实际意义。     

Non-linear DSGE models and the optimized central difference particle filter

We improve the accuracy and speed of particle filtering for non-linear DSGE models with potentially non-normal shocks. This is done by introducing a new proposal distribution which (i) incorporates information from new observables and (ii) has a small optimization step that minimizes the distance to the optimal proposal distribution. A particle filter with this proposal distribution is shown to deliver a high level of accuracy even with relatively few particles, and it is therefore much more efficient than the standard particle filter.     

Testing conditional symmetry against one-sided alternatives in square contingency tables

For a (k×k) square contingency table with ordered categories, letX(Y) denote the row (column) number. The conditional symmetry model is given byP(X=i, Y=j|X<Y)=P(X=j, Y=i |X>Y), ∀i<j. In this paper, we study the likelihood ratio tests of conditional symmetry in a square contingency table against two particular classes of one-sided alternatives. We obtain the maximum likelihood estimators under each alternative. The asymptotic null distributions of the likelihood ratio statistics are shown to have chi-bar square type distributions. A simulation study is performed by comparing the powers of different tests. The theory developed is illustrated by using the famous eye vision data from Stuart (1953).     

Three Sides of Smoothing: Categorical Data Smoothing, Nonparametric Regression, and Density Estimation

The past forty years have seen a great deal of research into the construction and properties of nonparametric estimates of smooth functions. This research has focused primarily on two sides of the smoothing problem: nonparametric regression and density estimation. Theoretical results for these two situations are similar, and multivariate density estimation was an early justification for the Nadaraya-Watson kernel regression estimator.
A third, less well-explored, strand of applications of smoothing is to the estimation of probabilities in categorical data. In this paper the position of categorical data smoothing as a bridge between nonparametric regression and density estimation is explored. Nonparametric regression provides a paradigm for the construction of effective categorical smoothing estimates, and use of an appropriate likelihood function yields cell probability estimates with many desirable properties. Such estimates can be used to construct regression estimates when one or more of the categorical variables are viewed as response variables. They also lead naturally to the construction of well-behaved density estimates using local or penalized likelihood estimation, which can then be used in a regression context. Several real data sets are used to illustrate these points.     

Data transforms with exponential smoothing methods of forecasting

In this paper, transforms are used with exponential smoothing, in the quest for better forecasts. Two types of transforms are explored: those which are applied to a time series directly, and those which are applied indirectly to the prediction errors. The various transforms are tested on a large number of time series from the M3 competition, and ANOVA is applied to the results. We find that the non-transformed time series is significantly worse than some transforms on the monthly data, and on a distribution-based performance measure for both annual and quarterly data.     

Reconfiguring a set of coverage-providing facilities under travel time uncertainty

We study networks of facilities that must provide coverage under conditions of uncertainty with respect to travel times and customer demand. We model this uncertainty through a set of scenarios. Since opening new facilities and/or closing existing ones is often quite expensive, we focus on optimal re-configuration of the network, that is finding a facility set that achieves desired thresholds with respect to expected and minimal coverage, while retaining as many of the existing facilities as possible. We illustrate our model with an example of Toronto Fire Service. We demonstrate that relocating just a few facilities can have the same effect as opening a similar number of new ones. We develop exact and approximate solution approaches and test them with computational experiments. Algorithm based on Tabu Search (with certain novel components) appears to be particularly successful for this problem. We also analyze the multi-objective version of the problem, where the expected and minimum coverage levels are treated as objectives in addition to the objective of maximizing the number of pre-existing facilities in the final location set.     

一种改进的中文分词算法

汉语自动分词是汉语信息处理的前提,词库是汉语自动分词的基础.文章提出了一种在对词库进行改造的基础上改进的匹配算法,突破了最大匹配分词算法分词的长度限制,提高了中文分词的速度和效率.     

Challenges for ground operations arising from aircraft concepts using alternative energy

Current research in the field of future aircraft concepts aims at accommodating ambitious reduction goals set by national and international regulators. These concepts should be investigated not only with regard to aircraft efficiency, but also in terms of their compatibility with airline operations, existing ground handling procedures and airport infrastructure requirements, as these influence the overall performance of a future aircraft concept. This paper addresses this aspect, focusing on case studies concerning hybrid-electric and universally-electric aircraft concepts, analyzing implications for current ground handling operations at the airport. Current bottlenecks, such as capacity shortages, and potential areas of improvement are identified based on a state-of-the-art reference ground handling process. To this end, requirements of different stakeholders, including airports, airlines and ground handling providers, are outlined. In the next step, insights are contrasted with operational requirements of the future aircraft concepts under consideration. The paper stresses the anticipated challenges involved in aligning future aircraft requirements with current procedures, discusses the necessary adaptions to operational processes. The results highlight changes that need to be made to the current system before an aircraft can enter service, and provide an initial basis for the strategic planning of the stakeholders involved.