Comparison of various statistical methods for detecting disease outbreaks

In this article, we compared seven statistical methods for detecting outbreaks of infectious disease; Historical limits, English model, SPOTv2, CuSums, Bayesian predictive model, RKI method and Serfling model. We used simulated data and real data to compare those seven methods. Simulated data have parameters such as trend, seasonality, mean and standard deviation. Among these methods, SPOTv2 shows the best performance with a balance between sensitivity and positive predictive value and short time lag. But in datasets having strong trends, Bayesian predictive model, English model and Serfling model perform better than SPOTv2. These methods are also compared through real numerical example.     

Comparison of point-by-point and group classification for the case of a multivariate normal distribution

The superiority of group classification over point-by-point classification is demonstrated. Some numerical results are presented.Translated from Statisticheskie Metody, pp. 3–7, 1982.     

Image data analysis and classification in marketing

Nowadays, the diffusion of smartphones, tablet computers, and other multipurpose equipment with high-speed Internet access makes new data types available for data analysis and classification in marketing. So, e.g., it is now possible to collect images/snaps, music, or videos instead of ratings. With appropriate algorithms and software at hand, a marketing researcher could simply group or classify respondents according to the content of uploaded images/snaps, music, or videos. However, appropriate algorithms and software are sparsely known in marketing research up to now. The paper tries to close this gap. Algorithms and software from computer science are presented, adapted and applied to data analysis and classification in marketing. The new SPSS-like software package IMADAC is introduced.     

Non parametric statistical models for on-line text classification

Social media, such as blogs and on-line forums, contain a huge amount of information that is typically unorganized and fragmented. An important issue, that has been raising importance so far, is to classify on-line texts in order to detect possible anomalies. For example on-line texts representing consumer opinions can be, not only very precious and profitable for companies, but can also represent a serious damage if they are negative or faked. In this contribution we present a novel statistical methodology rooted in the context of classical text classification, in order to address such issues. In the literature, several classifiers have been proposed, among them support vector machine and naive Bayes classifiers. These approaches are not effective when coping with the problem of classifying texts belonging to an unknown author. To this aim, we propose to employ a new method, based on the combination of classification trees with non parametric approaches, such as Kruskal?CWallis and Brunner?CDette?CMunk test. The main application of what we propose is the capability to classify an author as a new one, that is potentially trustable, or as an old one, that is potentially faked.     

A statistical criterion for deciding dynamic classification problems

We give a statistical criterion for deciding whether to reclassify dynamic objects on the basis of empirical distributions of pairwise nearness. To do this we use a generalization of a criterion of Kolmogorov-Smirnov type to the case of partly dependent elements in samples.Translated fromDinamicheskie Sistemy, No. 6, 1987, pp. 93–95.     

General mathematical programming formulations for the statistical classification problem

The study presents mathematical programming formulations for the statistical classification problem. The formulations maximize the number of observations that are properly categorized. The models are easily modified for any number of categories of classification. The formulation were tested on a standard problem.     

On summing permutations and some statistical properties

A closed form expression is obtained for the sum of all permutations of n objects taken r at a time. The average and variance of the permutations are derived and are shown to be proportional to the average and variance of the objects themselves. The proportionality constant is a function of only r, n and the base b and is independent of the actual objects considered. Previous results aimed at determining the sum of permutations are shown to be very specific cases of the current development.     

Clustering and maximum likelihood search for efficient statistical classification with medium-sized databases

This paper addresses the problem of insufficient performance of statistical classification with the medium-sized database (thousands of classes). Each object is represented as a sequence of independent segments. Each segment is defined as a random sample of independent features with the distribution of multivariate exponential type. To increase the speed of the optimal Kullback–Leibler minimum information discrimination principle, we apply the clustering of the training set and an approximate nearest neighbor search of the input object in a set of cluster medoids. By using the asymptotic properties of the Kullback–Leibler divergence, we propose the maximal likelihood search procedure. In this method the medoid to check is selected from the cluster with the maximal joint density (likelihood) of the distances to the previously checked medoids. Experimental results in image recognition with artificially generated dataset and Essex facial database prove that the proposed approach is much more effective, than an exhaustive search and the known approximate nearest neighbor methods from FLANN and NonMetricSpace libraries.