4,368 research outputs found
Accounting for outliers and calendar effects in surrogate simulations of stock return sequences
Surrogate Data Analysis (SDA) is a statistical hypothesis testing framework
for the determination of weak chaos in time series dynamics. Existing SDA
procedures do not account properly for the rich structures observed in stock
return sequences, attributed to the presence of heteroscedasticity, seasonal
effects and outliers. In this paper we suggest a modification of the SDA
framework, based on the robust estimation of location and scale parameters of
mean-stationary time series and a probabilistic framework which deals with
outliers. A demonstration on the NASDAQ Composite index daily returns shows
that the proposed approach produces surrogates that faithfully reproduce the
structure of the original series while being manifestations of linear-random
dynamics.Comment: 21 pages, 7 figure
Forecasting seasonality in prices of potatoes and onions: challenge between geostatistical models, neuro fuzzy approach and Winter method
This paper, we studied the ability of geostatistical models (ordinary kriging (OK) and Inverse distance weighting (IDW)), adaptive neuro-fuzzy inference system (ANFIS) and Winter method for prediction of seasonality in prices of potatoes and onions in Iran over the seasonal period 1986_2001. Results show that the best estimators in order are winter method, ANFIS and geostatistical methods. The results indicate that Winter and ANFIS had powerful results for prediction the prices while geostatistical models were not useful in this respect.Price; Geostatistical model; Kiriging; Inverse distance weighting; Winterâs method; Adaptive neuro fuzzy inference system; Potatoes; Onions; Iran
Forecasting seasonality in prices of potatoes and onions: challenge between geostatistical models, neuro fuzzy approach and Winter method
Price, Geostatistical model, Kiriging, Inverse distance weighting, Winterâs method, Adaptive neuro fuzzy inference system, Potatoes, Onions, Iran, Crop Production/Industries, Demand and Price Analysis,
Modeling Stroke Diagnosis with the Use of Intelligent Techniques
The purpose of this work is to test the efficiency of specific intelligent classification algorithms when dealing with the domain of stroke medical diagnosis. The dataset consists of patient records of the âAcute Stroke Unitâ, Alexandra Hospital, Athens, Greece, describing patients suffering one of 5 different stroke types diagnosed by 127 diagnostic attributes / symptoms collected during the first hours of the emergency stroke situation as well as during the hospitalization and recovery phase of the patients. Prior to the application of the intelligent classifier the dimensionality of the dataset is further reduced using a variety of classic and state of the art dimensionality reductions techniques so as to capture the intrinsic dimensionality of the data. The results obtained indicate that the proposed methodology achieves prediction accuracy levels that are comparable to those obtained by intelligent classifiers trained on the original feature space
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
Nonlinear shrinkage estimation of large-dimensional covariance matrices
Many statistical applications require an estimate of a covariance matrix
and/or its inverse. When the matrix dimension is large compared to the sample
size, which happens frequently, the sample covariance matrix is known to
perform poorly and may suffer from ill-conditioning. There already exists an
extensive literature concerning improved estimators in such situations. In the
absence of further knowledge about the structure of the true covariance matrix,
the most successful approach so far, arguably, has been shrinkage estimation.
Shrinking the sample covariance matrix to a multiple of the identity, by taking
a weighted average of the two, turns out to be equivalent to linearly shrinking
the sample eigenvalues to their grand mean, while retaining the sample
eigenvectors. Our paper extends this approach by considering nonlinear
transformations of the sample eigenvalues. We show how to construct an
estimator that is asymptotically equivalent to an oracle estimator suggested in
previous work. As demonstrated in extensive Monte Carlo simulations, the
resulting bona fide estimator can result in sizeable improvements over the
sample covariance matrix and also over linear shrinkage.Comment: Published in at http://dx.doi.org/10.1214/12-AOS989 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Three-structured smooth transition regression models based on CART algorithm
In the present work, a tree-based model that combines aspects of CART (Classification and Regression Trees) and STR (Smooth Transition Regression) is proposed. The main idea relies on specifying a parametric nonlinear model through a tree-growing procedure. The resulting model can be analysed either as a fuzzy regression or as a smooth transition regression with multiple regimes. Decisions about splits are entirely based on statistical tests of hypotheses and confidence intervals are constructed for the parameters within the terminal nodes as well as the final predictions. A Monte Carlo Experiment shows the estimatorsâ properties and the ability of the proposed algorithm to identify correctly several tree architectures. An application to the famous Boston Housing dataset shows that the proposed model provides better explanation with the same number of leaves as the one obtained with the CART algorithm.
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