http://folk.uib.no/eha070/mat262/papers/Rudin%20Osher%20Fatemi.pdf

Nonlinear total variation based noise removal algorithms

https://leeds-faculty.colorado.edu/glover/TS%20-%20Future%20Paths%20for%20Integer%20Programming.pdf

Future paths for integer programming and links to artificial intelligence

We present a set of stylized empirical facts emerging from the statistical analysis of price variations in various types of financial markets. We first discuss some general issues common to all statistical studies of financial time series. Various statistical properties of asset returns are then described: distributional properties, tail properties and extreme fluctuations, pathwise regularity, linear and nonlinear dependence of returns in time and across stocks. Our description emphasizes properties common to a wide variety of markets and instruments. We then show how these statistical properties invalidate many of the common statistical approaches used to study financial data sets and examine some of the statistical problems encountered in each case.

/pdf/empirical-properties-of-asset-returns-stylized-facts-and-3gbrgrs0vm.pdf

Empirical properties of asset returns: stylized facts and statistical issues

This book concerns the use of concepts from statistical physics in the description of financial systems. The authors illustrate the scaling concepts used in probability theory, critical phenomena, and fully developed turbulent fluids. These concepts are then applied to financial time series. The authors also present a stochastic model that displays several of the statistical properties observed in empirical data. Statistical physics concepts such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling permit an understanding of the global behaviour of economic systems without first having to work out a detailed microscopic description of the system. Physicists will find the application of statistical physics concepts to economic systems interesting. Economists and workers in the financial world will find useful the presentation of empirical analysis methods and well-formulated theoretical tools that might help describe systems composed of a huge number of interacting subsystems.

/pdf/introduction-to-econophysics-correlations-and-complexity-in-4a1wviggsu.pdf

Introduction to Econophysics: Correlations and Complexity in Finance

The natural complexity of ecological communities regularly lures ecologists to collect elaborate data sets in which confounding factors are often present. Although multiple regression is commonly used in such cases to test the individual effects of many explanatory variables on a continuous response, the inherent collinearity (multicollinearity) of confounded explanatory variables encumbers analyses and threatens their statistical and inferential interpretation. Using numerical simulations, I quantified the impact of multicollinearity on ecological multiple regression and found that even low levels of collinearity bias analyses (r ≥ 0.28 or r2 ≥ 0.08), causing (1) inaccurate model parameterization, (2) decreased statistical power, and (3) exclusion of significant predictor variables during model creation. Then, using real ecological data, I demonstrated the utility of various statistical techniques for enhancing the reliability and interpretation of ecological multiple regression in the presence of multic...

Confronting multicollinearity in ecological multiple regression

THE availability of high-frequency data for financial markets has made it possible to study market dynamics on timescales of less than a day1. For foreign exchange (FX) rates Muller et al.2 have shown that there is a net flow of information from long to short timescales: the behaviour of long-term traders (who watch the markets only from time to time) influences the behaviour of short-term traders (who watch the markets continuously). Motivated by this hierarchical feature, we have studied FX market dynamics in more detail, and report here an analogy between these dynamics and hydrodynamic turbulence3–8. Specifically, the relationship between the probability density of FX price changes (δx) and the time delay (δt) (Fig. la) is much the same as the relationship between the probability density of the velocity differences (δv) of two points in a turbulent flow and their spatial separation δr (Fig. 1b). Guided by this similarity we claim that there is an information cascade in FX market dynamics that corresponds to the energy cascade in hydrodynamic turbulence. On the basis of this analogy we can now rationalize the statistics of FX price differences at different time delays, which is important for, for example, option pricing. The analogy also provides a conceptual framework for understanding the short-term dynamics of speculative markets.

Turbulent cascades in foreign exchange markets

A.- Acceptance Region.- Accuracy.- Algorithm.- Alternative hypothesis.- Analysis of binary data.- Analysis of categorical data.- Analysis of residuals.- Analysis of variance.- Anderson Oskar.- Anderson Theodore W.- Anderson-Darling test.- Arithmetic mean.- Arithmetic triangle.- ARMA models.- Arrangement.- Attributable risk.- Autocorrelation and partial autocorrelation.- Avoidable risk.

The Concise Encyclopedia of Statistics

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG).

All about quantiles Financial statistics and time series Estimation, testing and characterization Deep in the data Classification Density estimation and image processing Regression models in environmental studies.

on Statistical data analysis based on the L1-norm and related methods

Linear Regression Analysis. Constructing and Checking the Model. Least Squares Regression. Least Absolute Deviations Regression. M-Regression. Nonparametric Regression. Bayesian Regression. Ridge Regression. Comparisons. Other Methods.

Yadolah Dodge

Papers

Turbulent cascades in foreign exchange markets

The Concise Encyclopedia of Statistics

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG).

on Statistical data analysis based on the L1-norm and related methods

Alternative methods of regression