Nonparametric Algorithm Of Pattern Recognition Research Articles

Asset-return momentum prediction through pattern recognition

Asset-price momentum effects are important to study in finance. A novel nonparametric pattern-recognition algorithm is proposed to identify intraday momentum based on price patterns. Specifically, a classification-regression model for predicting future price-movement patterns is designed by combining Bayesian analysis and a nonparametric density-estimation method (with different similarity measures). The model is applied to asset prices in both original and compressed forms through nonlinear dimension-reduction techniques. Regardless of the data form, this model’s predictive power is statistically and empirically significant. The model most effectively predicts asset prices with high levels of long-range dependence. Empirical analysis demonstrates that the intraday-momentum effect can occur over longer time horizons than the half-hour documented in the literature. The intraday-trading strategy based on the similarity-prediction model achieves an increased expected return and a Sharpe ratio of 11% and 0.7, respectively, compared to alternative benchmark models.

Application of a Nonparametric Pattern Recognition Algorithm to the Problem of Testing the Hypothesis of the Independence of Variables of Multi-Valued Functions

Application of a Nonparametric Pattern Recognition Algorithm to the Problem of Testing the Hypothesis of the Independence of Variables of Multi-Valued Functions

Information system of remote sensing data analysis

The information system is designed to solve the problems of analyzing remote sensing data and making decisions using non-parametric pattern recognition algorithms, the synthesis of which is based on kernel estimates of probability density. Initial information is compiled by training samples on the states of the investigated natural objects and their corresponding spectral data, which are presented in Microsoft Excel files. The application of the software provides the following functions: primary processing of remote sensing data to evaluate the main quantitative characteristics of the laws of distribution of random variables; testing of the hypothesis on distributions of multivariate random variables based on non-parametric pattern recognition algorithms; estimating the state of natural objects by spectral data; mapping of territories by state of natural objects based on spectral data of earth surface elements and their corresponding spatial coordinates. Results of the presented functional tasks are saved in the form of reports in graphic files and spreadsheets of Microsoft Excel. Software tools can be used in the study of various nature objects from remote sensing data.

Application of a nonparametric pattern recognition algorithm in the problem of testing the hypothesis about the independence of variables of ambiguous functions

Application of a nonparametric pattern recognition algorithm in the problem of testing the hypothesis about the independence of variables of ambiguous functions

Formation of Sets of Independent Components of a Multidimensional Random Variable Based on a Nonparametric Pattern Recognition Algorithm

Formation of Sets of Independent Components of a Multidimensional Random Variable Based on a Nonparametric Pattern Recognition Algorithm

A method of sequentially generating a set of components of a multidimensional random variable using a nonparametric pattern recognition algorithm

We study in which way a priori information on the independence of random variables affects the approximation accuracy of a nonparametric estimate of the Rosenblatt–Parzen probability density. A new technique for generating sets of independent components of a multidimensional random variable is proposed. The methodology is based on testing the hypotheses of the independence of combinations of the multidimensional random variable components using a two-alternative nonparametric kernel algorithm for pattern recognition corresponding to the maximum likelihood criterion. Classes correspond to the domains of definition of the probability densities of sets of independent and dependent components of the multidimensional random variable. Nonparametric statistics of the kernel type are used to estimate the probability densities. The choice of the bandwidths of the kernel estimates of the probability densities is made from the condition of the minimum root-mean-square criterion. The sequential procedure for generating a set of independent components begins with the analysis of paired combinations of components of a multidimensional random variable. For each pair of components, the probability of an error in recognizing classes corresponding to the assumptions of independence and dependence of the considered components is estimated. A pair of components with the maximum difference between these errors is determined. If the errors obtained do not differ significantly, then there are no independent components in the considered multivariate random variable. If there is a significant difference in the probability estimates of class recognition errors, a pair of independent components is established. These components are included in a three-component set of a multidimensional random variable. The analysis of their combinations is carried out in the same way, following the above-described procedure. The process of generating the set of independent components is stopped when no reliable difference occurs any more between the probabilities of errors in recognizing situations belonging to the accepted classes. In this case, the previous set of independent components is the desired result. In contrast to the traditional methodology based on the Pearson criterion, the proposed approach allows us to bypass a problem of the decomposition of the range of values of random variables into multidimensional intervals. The method of generating a set of independent components of a multidimensional random variable is illustrated by the results of the analysis of spectral features of remote sensing data of forest tracts using space imagery from the Landsat-8 satellite.

Nonparametric pattern recognition algorithm for testing a hypothesis of the independence of random variables

A new method for testing a hypothesis of the independence of multidimensional random variables is proposed. The technique under consideration is based on the use of a nonparametric pattern recognition algorithm that meets a maximum likelihood criterion. In contrast to the traditional formulation of the pattern recognition problem, there is no a priori training sample. The initial information is represented by statistical data, which are made up of the values of a multivariate random variable. The distribution laws of random variables in the classes are estimated according to the initial statistical data for the conditions of their dependence and independence. When selecting optimal bandwidths for nonparametric kernel-type probability density estimates, the minimum standard deviation is used as a criterion. Estimates of the probability of pattern recognition error in the classes are calculated. Based on the minimum value of the estimates of the probabilities of pattern recognition errors, a decision is made on the independence or dependence of the random variables. The technique developed is used in the spectral analysis of remote sensing data.

Formation of sets of independent components of a multidimensional random variable based on a nonparametric pattern recognition algorithm

The possibility of circumventing the problem of decomposition of the range of values of random variables when testing various hypotheses is considered. A brief review of the literature on this problem is given. A method for forming sets of independent components of a multidimensional random variable is proposed, based on hypotheses testing about the independence of paired combinations of components of a multidimensional random variable. The method uses a two-dimensional non-parametric algorithm for pattern recognition of the kernel type, corresponding to the criterion of maximum likelihood. In contrast to the traditional method based on the application of the Pearson criterion, the proposed approach avoids the problem of decomposing the range of values of random variables into multidimensional intervals. The results of computational experiments performed according to the method of forming sets of independent random variables are presented. Using the information obtained, an information graph is constructed, the vertices of which correspond to the components of a multidimensional random variable, and the edges determine their independence. Then the vertices of the complete subgraphs correspond to groups of independent components of a random variable. The obtained results form the basis for the synthesis of a multi-level nonparametric large volume data processing system, each level of which corresponds to a specific set of independent random variables.

Method of Fast Bandwidth Selection in a Nonparametric Classifier Corresponding to the a Posteriori Probability Maximum Criterion

A method of fast bandwidth selection in a nonparametric algorithm of pattern recognition corresponding to the maximum a posteriori probability criterion is proposed. The algorithm is based on the results of studying the asymptotic properties of the nonparametric estimate of the separation surface equation and probability densities in solving a two-alternative problem of pattern recognition. The proposed method is compared with the traditional approach based on minimizing the classification error probability estimate.

Methods for Rapid Selection of Kernel Function Blur Coefficients in a Nonparametric Pattern Recognition Algorithm

A fast algorithm is proposed for choosing the coefficients of blur coefficients for kernel functions in a nonparametric estimate of the separating surface equation for a two-alternative pattern recognition problem. The algorithm is based on the results of a study of the asymptotic properties of nonparametric estimates of the decision function in the recognition problem for patterns and the probability densities of the distribution of random variables in classes. We compare the proposed algorithm with the traditional approach based on minimizing the estimated probability of a classification error.

Method of fast algorithm bandwidth selection for kernel function in nonparametric pattern recognition algorithm

Method of fast algorithm bandwidth selection for kernel function in nonparametric pattern recognition algorithm

Nonparametric Algorithms for Estimating the States of Natural Objects

Modifications of a nonparametric pattern recognition algorithm corresponding to the maximum likelihood criterion with additional decision functions are considered. The synthesis of the proposed algorithms is based on the analysis of the ratios of the estimates of the probability density distributions of random variables in classes and their functionals with input thresholds. The choice of the thresholds is determined by specific features of the classification problem. The results obtained are applied for assessing the states of forest tracts on the basis of remote sensing data.

Testing of hypothesis of two-dimensional random variables independence on the basis of algorithm of pattern recognition

The new technique of testing of hypothesis of random variables independence is offered. Its basis is made by nonparametric algorithm of pattern recognition. The considered technique doesn't demand sampling of area of values of random variables.

Analysis of nonparametric pattern recognition algorithms under incomplete data

The nonparametric pattern recognition algorithms are investigated under incomplete data in the training sample. Requirements on the incomplete data filling methods are formulated by analyzing conditions of their asymptotic convergence.

Nonparametric pattern recognition algorithms for random values of fuzziness factors of kernel functions

Nonparametric pattern recognition algorithms based on a randomized optimization approach are proposed. The idea of the approach is to validate the random character of fuzziness factors of kernel functions and choose parameters of their distribution law by optimizing nonparametric decision rules. Properties of the developed qualifiers are investigated. Results of their comparing with conventional nonparametric pattern recognition algorithms are analyzed.

Testing the performance of some nonparametric pattern recognition algorithms in realistic cases

The success obtained by Statistical Pattern Recognition in many disciplines is certainly related to the quality and availability of many data, normally distributed. However, in other disciplines, the data sets consist of few measurements, often binned, correlated, and not normally distributed. Usually, we do not even know which features have an influence on the process. The main goal of this paper is to evaluate the performance of some nonparametric Pattern Recognition algorithms when applied to such data. Finally we show the results of the application of the four nonparametric statistical pattern recognition techniques to real volcanological data.

Statistical identification of physical patterns which accompany eruptive activity on Mount Etna, Sicily

Abstract A new statistical nonparametric pattern recognition algorithm is used to identify the phenomenology which accompanies summit and flank eruptions on Mount Etna, Sicily. This algorithm allows us to solve some of the operational problems typical of logic-type pattern recognition algorithms. Volcanic activity is parametrized through 12 variables concerning seismicity, past eruptive history, and precipitation, which practically exhaust the presently available data. In the 40 days which precede and follow the onset of each eruption, we identified no pattern whatsoever for summit eruptions, and a single significant pattern for flank activity. This indicates different eruptive mechanisms for flank and summit events. The latter appear to be independent of all the phenomena considered, and should therefore be ascribed either to magma feeding processes, a variable which we were not able to consider in our analysis due to the lack of data, or to random behaviour, without any repetitive scheme. Flank activity appears to be linked to the state of regional tectonic stress, but is independent of past eruptive history and groundwater level. This means that eruptions of the size Etna recently experienced, do not perturb the system, which must have a much larger capacity, at least of the order of 10 9 m 3 .