Prediction focussed model selection for autoregressive models.

In order to make predictions of future values of a time series, one needs to specify a forecasting model. A popular choice is an autoregressive time series model, where the order of the model is chosen by an information criterion. We propose an extension of the Focussed Information Criterion (FIC) for model-order selection with focus on a high predictive accuracy (i.e.themeansquaredforecasterrorislow). We obtain theoretical results and illustrate in a simulation study that this FIC can outperform classical order selection criteria in the setting with one series to predict and a different series for parameter estimation. We also demonstrate, via a simulation study and some real data examples, that in the practical setting of only one available time series, the performance of the FIC is comparable to the performance of other information criteria.Choice; Criteria; Data; Focussed information criterion; Forecasting; Information; Model; Model selection; Models; Order; Performance; Prediction; Predictions; Selection; Simulation; Studies; Time; Time series; Value;

The Adequateness of Wavelet Based Model for Time Series

In general, time series is modeled as summation of known information i.e. historical information components, and unknown information i.e. random component. In wavelet based model, time series is represented as linear model of wavelet coecients. Wavelet based model captures the time series feature perfectly when the historical information components dominate the process. In other hand, it has low enforcement when the random component dominates the process. This paper proposes an eort to develop the adequateness of wavelet based model, when the random component dominated the process. By weighted summation, the data is carried to the new form which has higher dependencies. Consequently, wavelet based model will work better. Finally, it is hoped that the better prediction of wavelet based model will be carried to the original prediction in reverting process

On the Information Rates of the Plenoptic Function

The {\it plenoptic function} (Adelson and Bergen, 91) describes the visual information available to an observer at any point in space and time. Samples of the plenoptic function (POF) are seen in video and in general visual content, and represent large amounts of information. In this paper we propose a stochastic model to study the compression limits of the plenoptic function. In the proposed framework, we isolate the two fundamental sources of information in the POF: the one representing the camera motion and the other representing the information complexity of the "reality" being acquired and transmitted. The sources of information are combined, generating a stochastic process that we study in detail. We first propose a model for ensembles of realities that do not change over time. The proposed model is simple in that it enables us to derive precise coding bounds in the information-theoretic sense that are sharp in a number of cases of practical interest. For this simple case of static realities and camera motion, our results indicate that coding practice is in accordance with optimal coding from an information-theoretic standpoint. The model is further extended to account for visual realities that change over time. We derive bounds on the lossless and lossy information rates for this dynamic reality model, stating conditions under which the bounds are tight. Examples with synthetic sources suggest that in the presence of scene dynamics, simple hybrid coding using motion/displacement estimation with DPCM performs considerably suboptimally relative to the true rate-distortion bound.Comment: submitted to IEEE Transactions in Information Theor

The Most Influential Paper Gerard Salton Never Wrote

Gerard Salton is often credited with developing the vector space model (VSM) for information retrieval (IR). Citations to Salton give the impression that the VSM must have been articulated as an IR model sometime between 1970 and 1975. However, the VSM as it is understood today evolved over a longer time period than is usually acknowledged, and an articulation of the model and its assumptions did not appear in print until several years after those assumptions had been criticized and alternative models proposed. An often cited overview paper titled ???A Vector Space Model for Information Retrieval??? (alleged to have been published in 1975) does not exist, and citations to it represent a confusion of two 1975 articles, neither of which were overviews of the VSM as a model of information retrieval. Until the late 1970s, Salton did not present vector spaces as models of IR generally but rather as models of specifi c computations. Citations to the phantom paper refl ect an apparently widely held misconception that the operational features and explanatory devices now associated with the VSM must have been introduced at the same time it was fi rst proposed as an IR model.published or submitted for publicatio

Construction Procurement: Modelling Bidders’ Learning in Recurrent Bidding

Construction remains a significant area of public expenditure. An understanding of the process of changes in construction pricing, and how the process can be manipulated through the release of bidding feedback information is vital, in order to best design clients’ procurement policies. This paper aims to statistically model inexperienced individual bidders’ learning in recurrent bidding under partial and full information feedback conditions. Using an experimental dataset, the developed linear mixed model contains three predictor variables, namely: time factor, information feedback conditions, and bidding success rate in the preceding round. The results show nonlinearity and curvature in the bidders’ learning curves. They are generally less competitive in time periods after a winning bid with lower average bids submitted by those subjected to full information feedback condition. In addition, the model has captured the existence of heterogeneity across bidders with individual-specific parameter estimates that demonstrate the uniqueness of individual bidders’ learning curves in recurrent bidding. The findings advocate for adequate bidding feedback information in clients’ procurement design to facilitate learning among contractors, which may in turn lead to increased competitiveness in their bids