Estimation of Individual Micro Data from Aggregated Open Data

In this paper, we propose a method of estimating individual micro data from aggregated open data based on semi-supervised learning and conditional probability. Firstly, the proposed method collects aggregated open data and support data, which are related to the individual micro data to be estimated. Then, we perform the locality sensitive hashing (LSH) algorithm to find a subset of the support data that is similar to the aggregated open data and then classify them by using the Ensemble classification model, which is learned by semi-supervised learning. Finally, we use conditional probability to estimate the individual micro data by finding the most suitable record for the probability distribution of the individual micro data among the classification results. To evaluate the performance of the proposed method, we estimated the individual building data where the fire occurred using the aggregated fire open data. According to the experimental results, the micro data estimation performance of the proposed method is 59.41% on average in terms of accuracy.Comment: 7 page

Estimation of Production Functions using Average Data

Agricultural economists rely on aggregated data at various levels depending on data availability and the econometric techniques employed. However, the implication of aggregation on economic relationships remains an open question. To examine the impact of aggregation on estimation, Monte Carlo techniques and data are employed on production practices.Research Methods/ Statistical Methods,

Aggregation and long memory: recent developments

It is well-known that the aggregated time series might have very different properties from those of the individual series, in particular, long memory. At the present time, aggregation has become one of the main tools for modelling of long memory processes. We review recent work on contemporaneous aggregation of random-coefficient AR(1) and related models, with particular focus on various long memory properties of the aggregated process

Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction

Frame-level visual features are generally aggregated in time with the techniques such as LSTM, Fisher Vectors, NetVLAD etc. to produce a robust video-level representation. We here introduce a learnable aggregation technique whose primary objective is to retain short-time temporal structure between frame-level features and their spatial interdependencies in the representation. Also, it can be easily adapted to the cases where there have very scarce training samples. We evaluate the method on a real-fake expression prediction dataset to demonstrate its superiority. Our method obtains 65% score on the test dataset in the official MAP evaluation and there is only one misclassified decision with the best reported result in the Chalearn Challenge (i.e. 66:7%) . Lastly, we believe that this method can be extended to different problems such as action/event recognition in future.Comment: Submitted to International Conference on Computer Vision Workshop

Putting Iterative Proportional Fitting on the researcher’s desk

‘Iterative Proportional Fitting’ (IPF) is a mathematical procedure originally developed to combine the information from two or more datasets. IPF is a well-established technique with the theoretical and practical considerations behind the method thoroughly explored and reported. In this paper the theory of IPF is investigated with a mathematical definition of the procedure and a review of the relevant literature given. So that IPF can be readily accessible to researchers the procedure has been automated in Visual Basic and a description of the program and a ‘User Guide’ are provided. IPF is employed in various disciplines but has been particularly useful in census-related analysis to provide updated population statistics and to estimate individual-level attribute characteristics. To illustrate the practical application of IPF various case studies are described. In the future, demand for individual-level data is thought likely to increase and it is believed that the IPF procedure and Visual Basic program have the potential to facilitate research in geography and other disciplines

Analysing welfare reform in a microsimulation-AGE model: the value of disaggregation

We present a combined, consistent microsimulation-AGE model that uses the labour market model PACE-L, data from the German Socio-Economic Panel and a discrete choice labour supply estimation. The model is used to analyse a reform that cuts the social assistance minimum income and lowers the transfer withdrawal rate in order to encourage labour force participation at the lower end of the wage distribution. We compare a disaggregated and an aggregated version of the model as well as a partial and a general equilibrium variant. It turns out that both disaggregation and general equilibrium feedback tend to mitigate the labour supply response to the reform proposal. While some labour supply indicators react quite sensitively to the level of aggregation, most macroeconomic variables are considerably more robust. --applied general equilibrium,discrete working time choice,labour market,wage bargaining,labour market reform,logit model,microsimulation

Long-memory process and aggregation of AR(1) stochastic processes: A new characterization

Contemporaneous aggregation of individual AR(1) random processes might lead to different properties of the limit aggregated time series, in particular, long memory (Granger, 1980). We provide a new characterization of the series of autoregressive coefficients, which is defined from the Wold representation of the limit of the aggregate stochastic process, in the presence of long-memory features. Especially the infinite autoregressive stochastic process defined by the almost sure representation of the aggregate process has a unit root in the presence of the long-memory property. Finally we discuss some examples using some well-known probability density functions of the autoregressive random parameter in the aggregation literature. JEL Classification Code: C2, C13