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.     

Tests of independence in a bivariate exponential distribution

The problem of testing independence in a two component series system is considered. The joint distribution of component lifetimes is modeled by the Pickands bivariate exponential distribution, which includes the widely used Marshall and Olkins distribution and the Gumbels type II distribution. The case of identical components is first addressed. Uniformly most powerful unbiased test (UMPU) and likelihood ratio test are obtained. It is shown that inspite of a nuisance parameter, the UMPU test is unconditional and this test turns out to be the same as the likelihood ratio test. The case of nonidentical components is also addressed and both UMPU and likelihood ratio tests are obtained. A UMPU test is obtained to test the identical nature of the components and extensions to the type II censoring scheme and multiple component systems are also discussed. Some modifications to account for the difference in parameters under test and use conditions are also discussed.     

On the simultaneous estimation of physical and monetary commodity flows

Large databases mapping commodity flows measured in various units such as currency, tons or caloric values are the backbone of many recent environmental-economic studies. Their construction typically requires combining large amounts of partial information in a series of successive steps. These include the estimation of unobserved flows, transformations between units, handling aggregation re-classification and, finally, reconciling estimates with mass, financial and/or energy balances. This paper proposes a maximum entropy model that allows for the simultaneous estimation of unobserved commodity flows as well as corresponding prices such that data constraints in various units of measurement, levels of aggregation and possibly mismatching classifications are simultaneously satisfied. Its capability is assessed through a Monte-Carlo analysis and its performance compared with a simple step-wise approach. Our results suggest that the simultaneous approach performs significantly better in a vast majority of cases.     

单一出厂定价制度下的厂商区位选择

修改Hotelling(1929)模型的基本假定,假定厂商边际生产成本为正,交通成本由消费者负担,厂商区位可以为内生变量,也可以为外生变量,在此假定前提下,分析厂商的最优的区位—价格策略,以探讨最大差异原则或者最小差异化原则何时成立,或者不成立。     

A note on multinomial maximum likelihood estimation under ordered restrictions and the EM algorithm

A frequently occurring problem is to find the maximum likelihood estimation (MLE) of p subject to p∈C (C⊂ P the probability vectors in R ^k ). The problem has been discussed by many authors and they mainly focused when p is restricted by linear constraints or log-linear constraints. In this paper, we construct the relationship between the the maximum likelihood estimation of p restricted by p∈C and EM algorithm and demonstrate that the maximum likelihood estimator can be computed through the EM algorithm (Dempster et al. in J R Stat Soc Ser B 39:1–38, 1997). Several examples are analyzed by the proposed method.     

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.     

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.     

Calibrating the exponential Ornstein–Uhlenbeck multiscale stochastic volatility model

This paper demonstrates a tractable and efficient way of calibrating a multiscale exponential Ornstein–Uhlenbeck stochastic volatility model including a correlation between the asset return and its volatility. As opposed to many contributions where this correlation is assumed to be null, this framework allows one to describe the leverage effect widely observed in equity markets. The resulting model is non-exponential and driven by a degenerate noise, thus requiring a high level of care in designing the estimation algorithm. The way this difficulty is overcome provides guidelines concerning the development of an estimation algorithm in a non-standard framework. The authors propose using a block-type expectation maximization algorithm along with particle smoothing. This method results in an accurate calibration process able to identify up to three timescale factors. Furthermore, we introduce an intuitive heuristic which can be used to choose the number of factors.     

A mixture of Gaussians approach to mathematical portfolio oversight: the EF3M algorithm

An analogue can be made between: (a) the slow pace at which species adapt to an environment, which often results in the emergence of a new distinct species out of a once homogeneous genetic pool and (b) the slow changes that take place over time within a fund, mutating its investment style. A fund’s track record provides a sort of genetic marker, which we can use to identify mutations. This has motivated our use of a biometric procedure to detect the emergence of a new investment style within a fund’s track record. In doing so, we answer the question: What is the probability that a particular PM’s performance is departing from the reference distribution used to allocate her capital? The EF3M algorithm, inspired by evolutionary biology, may help detect early stages of an evolutionary divergence in an investment style and trigger a decision to review a fund’s capital allocation.