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The main aim for this paper is motivated by the usefulness of genetic algorithms (GAs) for the fitting of distribution models to financial market data. In detail, we use a GA along with the least squares method in order to achieve a more relatively accurate and robust approach for optimizing non-linear objective functions. The combination of these two methods is applied for fitting parametric distributions to a dataset of market index returns, improving the methodology of cumulative returns prediction. The process of extrapolation plays a fundamental role in this area of analysis, being essential to empirically fit a convenient distribution that describes the available data as closely as possible. For comparison and illustrative purpose, we analyse distribution models used in the financial literature for modelling such dataset, and then the practical application is carried out again on a more updated dataset from the same financial index. In addition, a brief simulation study is developed to illustrate the usefulness of the proposal procedure. [ABSTRACT FROM AUTHOR]

Three-phase induction motors are extensively used in the industry due to their robustness characteristics, low cost and easy maintenance. Usually, it is necessary to implement drive and control systems for such motors, which requires the knowledge of their mechanical and electrical parameters. However, in some cases, these data are not immediately available, or the values of the parameters may change due to the wear of motor components. Such problems can be circumvented if an efficient parameter estimation technique is available. In order to automatically estimate the parameters efficiently, the present work proposes a method, based on the differential evolution algorithm, aimed at the estimation of the electrical and mechanical parameters of three-phase induction motors. Such algorithm is capable of estimating the parameters of the equivalent electrical circuit, such as stator and rotor resistances and leakage inductances, the magnetizing inductance, and also mechanical parameters, such as moment of inertia and the friction coefficient. The performance of the proposed parameter estimation technique is evaluated for three different input signals: (i) current signal of a phase associated with the speed measured from a tachogenerator, (ii) current signal of a phase associated with the speed acquired from a torquemeter, and (iii) only the current signal of one phase. Finally, a series of simulated and experimental results are presented to validate the proposed technique, and the results show the good performance of the proposed strategies. [ABSTRACT FROM AUTHOR]

Condition monitoring aims at ensuring system safety which is a fundamental requirement for industrial applications and that has become an inescapable social demand. This objective is attained by instrumenting the system and developing data analytics methods such as statistical models able to turn data into relevant knowledge. One difficulty is to be able to correctly estimate the parameters of those methods based on time-series data. This paper suggests the use of the Weighted Distribution Theory together with the Expectation–Maximization algorithm to improve parameter estimation in statistical models with latent variables with an application to health monotonic under uncertainty. The improvement of estimates is made possible by incorporating uncertain and possibly noisy prior knowledge on latent variables in a sound manner. The latent variables are exploited to build a degradation model of dynamical system represented as a sequence of discrete states. Examples on Gaussian Mixture Models, Hidden Markov Models (HMM) with discrete and continuous outputs are presented on both simulated data and benchmarks using the turbofan engine datasets. A focus on the application of a discrete HMM to health monitoring under uncertainty allows to emphasize the interest of the proposed approach in presence of different operating conditions and fault modes. It is shown that the proposed model depicts high robustness in presence of noisy and uncertain prior. [ABSTRACT FROM AUTHOR]

In the past, electric distribution systems have lacked measurement points, and equipment is often operated to its failure point, resulting in customer outages. The widespread deployment of sensors improves distribution level observability. This paper presents an off-line parameter tracking procedure that leverages the increased deployment of distribution level measurement devices to estimate changes in impedance parameters over time. Parameter tracking enables the discovery of non-diurnal and non-seasonal changes, which can be flagged for investigation. The presented method uses an unbalanced distribution-system state-estimator and a measurement-residual based parameter-estimation procedure. Measurement residuals from multiple measurement snapshots are combined to increase effective local redundancy and improve robustness to measurement noise. The input data used in the experiments consists of data from devices on the primary distribution system and from customer meters, via an AMI system. Results of simulations on the IEEE 13-Node Test Feeder with 307 measurements and 246 parameters are presented to illustrate the proposed approach applied to changes in series impedance parameters. The proposed approach can detect a 5% change in series resistance elements with 2% measurement error using less than 1 day of measurement snapshots for a single estimate. [ABSTRACT FROM AUTHOR]

In the multiple linear regression, multicollinearity and outliers are commonly occurring problems. They produce undesirable effects on the ordinary least squares estimator. Many alternative parameter estimation methods are available in the literature which deals with these problems independently. In practice, it may happen that the multicollinearity and outliers occur simultaneously. In this article, we present a new estimator called as Linearized Ridge M-estimator which combats the problem of simultaneous occurrence of multicollinearity and outliers. A real data example and a simulation study is carried out to illustrate the performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

The performance of the state estimation (SE) depends on the accuracy of the received measured data, as well as the parameter data of the power system. In this paper, a new algorithm is proposed for the simultaneous identification and correction of measurement and branch parameter errors (series and shunt admittances) in the power system SE problem. The proposed method uses Lagrange multipliers for the identification and correction of branch parameter errors without the need for a-priori specification of suspect parameter vectors. Erroneous measurements and branch parameter values can be corrected using the proposed method to estimate the measurement and branch parameter errors. Finally, IEEE 14-, 30-, and 57-bus test systems are used to show the validity and robustness of the proposed algorithm. Single, multiple, and simultaneous errors in the conventional measurements and branch parameters are considered for different case studies. [ABSTRACT FROM AUTHOR]

Background: Caesarean sections (CS) rates continue to increase worldwide without a clear understanding of the main drivers and consequences. The lack of a standardized internationally-accepted classification system to monitor and compare CS rates is one of the barriers to a better understanding of this trend. The Robson's 10-group classification is based on simple obstetrical parameters (parity, previous CS, gestational age, onset of labour, fetal presentation and number of fetuses) and does not involve the indication for CS. This classification has become very popular over the last years in many countries. We conducted a systematic review to synthesize the experience of users on the implementation of this classification and proposed adaptations. Methods: Four electronic databases were searched. A three-step thematic synthesis approach and a qualitative metasummary method were used. Results: 232 unique reports were identified, 97 were selected for full-text evaluation and 73 were included. These publications reported on the use of Robson's classification in over 33 million women from 31 countries. According to users, the main strengths of the classification are its simplicity, robustness, reliability and flexibility. However, missing data, misclassification of women and lack of definition or consensus on core variables of the classification are challenges. To improve the classification for local use and to decrease heterogeneity within groups, several subdivisions in each of the 10 groups have been proposed. Group 5 (women with previous CS) received the largest number of suggestions. Conclusions: The use of the Robson classification is increasing rapidly and spontaneously worldwide. Despite some limitations, this classification is easy to implement and interpret. Several suggested modifications could be useful to help facilities and countries as they work towards its implementation. [ABSTRACT FROM AUTHOR]

Abstract: Acoustic imaging is a standard technique for mapping acoustic source powers and positions from limited observations on microphone sensors, which often causes an ill-conditioned inverse problem. In this article, we firstly improve the forward model of acoustic power propagation by considering background noises at the sensor array, and the propagation uncertainty caused by wind tunnel effects. We then propose a robust super-resolution approach via sparsity constraint for acoustic imaging in strong background noises. The sparsity parameter is adaptively derived from the sparse distribution of source powers. The proposed approach can jointly reconstruct source powers and positions, as well as the background noise power. Our approach is compared with the conventional beamforming, deconvolution and sparse regularization methods by simulated, wind tunnel data and hybrid data respectively. It is feasible to apply the proposed approach for effectively mapping monopole sources in wind tunnel tests. [Copyright &y& Elsevier]

Abstract: This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as “partial labeling” of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint framework and derive the corresponding EM updates accordingly. In our simulations, we observe that using this side information, we considerably improve the state recognition performance, up to 70%, with respect to the “achievable margin” defined by the baseline algorithms. Moreover, our algorithm is shown to be robust to the training conditions. [Copyright &y& Elsevier]

This article studiesM-type estimators for fitting robust generalized additive models in the presence of anomalous data. A new theoretical construct is developed to connect the costlyM-type estimation with least-squares type calculations. Its asymptotic properties are studied and used to motivate a computational algorithm. The main idea is to decompose the overallM-type estimation problem into a sequence of well-studied conventional additive model fittings. The resulting algorithm is fast and stable, can be paired with different nonparametric smoothers, and can also be applied to cases with multiple covariates. As another contribution of this article, automatic methods for smoothing parameter selection are proposed. These methods are designed to be resistant to outliers. The empirical performance of the proposed methodology is illustrated via both simulation experiments and real data analysis. Supplementary materials are available online. [ABSTRACT FROM AUTHOR]

Empirical Bayes methods have been extensively used for microarray data analysis by modeling the large number of unknown parameters as random effects. Empirical Bayes allows borrowing information across genes and can automatically adjust for multiple testing and selection bias. However, the standard empirical Bayes model can perform poorly if the assumed working prior deviates from the true prior. This paper proposes a new rank-conditioned inference in which the shrinkage and confidence intervals are based on the distribution of the error conditioned on rank of the data. Our approach is in contrast to a Bayesian posterior, which conditions on the data themselves. The new method is almost as efficient as standard Bayesian methods when the working prior is close to the true prior, and it is much more robust when the working prior is not close. In addition, it allows a more accurate (but also more complex) non-parametric estimate of the prior to be easily incorporated, resulting in improved inference. The new method’s prior robustness is demonstrated via simulation experiments. Application to a breast cancer gene expression microarray dataset is presented. Our R package rank.Shrinkage provides a ready-to-use implementation of the proposed methodology. [ABSTRACT FROM PUBLISHER]

How to determine the scale parameter and the cluster number are two important open issues of spectral clustering remained to be studied. In this paper, it is aimed to overcome these two problems. Firstly, we analyze the principle of spectral clustering from normalized cut. Secondly, on one hand, a weighted local scale was proposed to improve both the classification performance and robustness. On the other hand, we proposed an automatic cluster number estimation method from standpoint of Eigenvectors of its affinity matrix. Finally, a framework of robust and smart spectral clustering method was concluded; it is robust enough to deal with arbitrary distributed datasets and smart enough to estimate cluster number automatically. The proposed method was tested both on artificial datasets and UCI datasets, and experiments prove its availability. [ABSTRACT FROM AUTHOR]

In this paper, we develop robust estimation for the mean and covariance jointly for the regression model of longitudinal data within the framework of generalized estimating equations (GEE). The proposed approach integrates the robust method and joint mean-covariance regression modeling. Robust generalized estimating equations using bounded scores and leverage-based weights are employed for the mean and covariance to achieve robustness against outliers. The resulting estimators are shown to be consistent and asymptotically normally distributed. Simulation studies are conducted to investigate the effectiveness of the proposed method. As expected, the robust method outperforms its non-robust version under contaminations. Finally, we illustrate by analyzing a hormone data set. By downweighing the potential outliers, the proposed method not only shifts the estimation in the mean model, but also shrinks the range of the innovation variance, leading to a more reliable estimation in the covariance matrix. [ABSTRACT FROM AUTHOR]

In the Bayesian modelling the data and the prior information concerning a certain parameter of interest may conflict, in the sense that the information carried by them disagree. The most common form of conflict is the presence of outlying information in the data, which may potentially lead to wrong posterior conclusions. To prevent this problem we use robust models which aim to control the influence of the atypical information in the posterior distribution. Roughly speaking, we conveniently use heavy-tailed distributions in the model in order to resolve conflicts in favour of those sources of information which we believe is more credible. The class of heavy-tailed distributions is quite wide and the literature have been concerned in establishing conditions on the data and prior distributions in order to reject the outlying information. In this work we focus on the subexponential and $$\mathfrak L $$ classes of heavy-tailed distributions, in which we establish sufficient conditions under which the posterior distribution automatically rejects the conflicting information. [ABSTRACT FROM AUTHOR]

In Robust Parameter Design (RPD) the means and the covariances of noise variables, commonly assumed as known, are estimated from operating or historical data and hence can involve considerable sampling variability. In addition, for cases where there are noise factors that are measurable or with strong autocorrelation a more effective control strategy is to update the estimates of noise factor as the production takes place. This article presents a Bayesian approach to online RPD that accounts for uncertainty in noise factor and response models and allows the user to update the model estimates with production data and achieve more effective control performance. The proposed method is compared to existing dual response and certainty equivalence control approaches from the literature. Simulation examples and a case study that uses real manufacturing data from an injection molding process are used to demonstrate the proposed method. [ABSTRACT FROM AUTHOR]

The classical method, Ordinary Least Squares (OLS) is used to estimate the parameters of the linear regression, when assumptions are available, and its estimators have good properties, like unbiasedness, minimum variance, consistency, and so on. The alternative statistical techniques have been developed to estimate the parameters, when the data is contaminated with outliers. These are the robust (or resistant) methods. In this paper, three of robust methods are studied, which are: Maximum likelihood type estimate M-estimator, Modified Maximum likelihood type estimate MM-estimator and Least Trimmed Squares LTS-estimator, and their results are compared with OLS method. These methods applied to real data taken from the Charstin company for manufacturing furniture and wooden doors, the obtained results compared by using the criteria: Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Sum of Absolute Error (MSAE). The important conclusions that this study came up with are: the number of outlier values detected by using the four methods in the data for furniture's line are very close. This refers to the fact that the distribution of standard errors is close to the normal, but the outlier values found in the data for doors line, by using OLS are less than which detected by robust methods. This means that the distribution of standard errors is departure distant from the normal. The other important conclusion is that estimated values of parameters by using OLS are very far from its estimated values by using the robust methods with respect to doors line, the LTS-estimator gave better results by using MSE criterion, and M-estimator gave better results by using MAPE criterion. Further more, it has noticed that by using the criterion MSAE, the MM-estimator is better. The programs S-plus (version 8.0, professional 2007), Minitab (version 13.2) and SPSS (version 17) are used to analyze the data. [ABSTRACT FROM AUTHOR]

Abstract: Active queue management has been proved to be an effective mechanism at the intermediate nodes to alleviate network congestion. A novel robust active queue management scheme based on H-infinity feedback control theory is proposed in this paper. A TCP model with link capacity disturbances, modeling uncertainties and time-varying delay is investigated, and the objective is to design a feedback controller which guarantee the queue length stability and robustness against the external disturbances and model perturbations. Meanwhile a simple on-line estimation of TCP window size is adopted instead of state observer which reduces computation burden significantly. The controller parameters are obtained through the standard linear matrix inequality techniques. Finally NS-2 simulation results demonstrate effectiveness of the proposed method. [Copyright &y& Elsevier]

Long-range dependence (LRD) is discovered in time series arising from different fields, especially in network traffic and econometrics. Detecting the presence and the intensity of LRD plays a crucial role in time-series analysis and fractional system identification. The existence of LRD is usually indicated by the Hurst parameters. Up to now, many Hurst parameter estimators have been proposed in order to identify the LRD property involved in a time series. Since different estimators have different accuracy and robustness performances, in this study, 13 most popular Hurst parameter estimators are summarised and their estimation performances are investigated. LRD processes with known Hurst parameters are generated as the control data set for the robustness evaluation. In addition, three types of LRD processes are also obtained as the test signals by adding noises in terms of means, trends and seasonalities to the control data set. All 13 Hurst parameter estimators are applied to these LRD processes to estimate the existing Hurst parameters. The estimation results are documented and quantified by the standard errors. Conclusions of the accuracy and robustness performances of the estimators are drawn by comparing the estimation results. [ABSTRACT FROM AUTHOR]

Abstract: A complex near surface can impact the quality of land data, despite the fact that the geology below the near surface complexities can be laterally smooth. Redatuming with operators based on a high-frequency parameterization in terms of traveltimes and simple geometric spreading factors will fall short if there are large lateral and vertical variations in propagation velocity and buried anomalies in the near surface layer. Recently, a method was introduced that can estimate the involved redatuming operators in a full-waveform sense. Application to synthetic data shows an uplift to the redatuming quality, but residual imprint is still observed. In this paper an extension is introduced that estimates redatuming operators based on two datum reflections simultaneously. In this way the full-waveform estimation process becomes more robust and shows another level of improvement for synthetic data. This conclusion is further amplified by the results obtained on field data from an area with a severe near-surface problem. [Copyright &y& Elsevier]

Abstract: High breakdown estimation allows one to get reasonable estimates of the parameters from a sample of data even if that sample is contaminated by large numbers of awkwardly placed outliers. A drawback of high breakdown estimation techniques is the increased computational effort often needed compared to nonhigh breakdown estimation methods. Robust estimators possessing the exact fit property, for example, are NP-hard to compute. There is no hope to compute exact solutions for large high dimensional data sets. To tackle this problem, search heuristics are used to compute NP-hard estimators in high dimensions. Here, a threshold accepting algorithm that is applicable to different high breakdown estimators is presented. Further, variants of this threshold accepting algorithm for selected estimators—most prominently least trimmed squares and least median of squares—are introduced and shown to outperform existing popular search heuristics in difficult data situations. The results increase the applicability of robust methods and underline the usefulness of heuristics computation for computational statistics. [Copyright &y& Elsevier]

Abstract: It is natural to assume that a missing-data mechanism depends on latent variables in the analysis of incomplete data in latent variate modeling because latent variables are error-free and represent key notions investigated by applied researchers. Unfortunately, the missing-data mechanism is then not missing at random (NMAR). In this article, a new estimation method is proposed, which leads to consistent and asymptotically normal estimators for all parameters in a linear latent variate model, where the missing mechanism depends on the latent variables and no concrete functional form for the missing-data mechanism is used in estimation. The method to be proposed is a type of multi-sample analysis with or without mean structures, and hence, it is easy to implement. Complete-case analysis is shown to produce consistent estimators for some important parameters in the model. [Copyright &y& Elsevier]

There are copula-based statistical models in the literature for regression with dependent data such as clustered and longitudinal overdispersed counts, for which parameter estimation and inference are straightforward. For situations where the main interest is in the regression and other univariate parameters and not the dependence, we propose a “weighted scores method”, which is based on weighting score functions of the univariate margins. The weight matrices are obtained initially fitting a discretized multivariate normal distribution, which admits a wide range of dependence. The general methodology is applied to negative binomial regression models. Asymptotic and small-sample efficiency calculations show that our method is robust and nearly as efficient as maximum likelihood for fully specified copula models. An illustrative example is given to show the use of our weighted scores method to analyze utilization of health care based on family characteristics. [ABSTRACT FROM PUBLISHER]

Functional mapping has been a powerful tool in mapping quantitative trait loci (QTL) underlying dynamic traits of agricultural or biomedical interest. In functional mapping, multivariate normality is often assumed for the underlying data distribution, partially due to the ease of parameter estimation. The normality assumption however could be easily violated in real applications due to various reasons such as heavy tails or extreme observations. Departure from normality has negative effect on testing power and inference for QTL identification. In this work, we relax the normality assumption and propose a robust multivariate t-distribution mapping framework for QTL identification in functional mapping. Simulation studies show increased mapping power and precision with the t distribution than that of a normal distribution. The utility of the method is demonstrated through a real data analysis. [ABSTRACT FROM AUTHOR]

Abstract: We investigate the performance of linearly penalized likelihood estimators for estimating distributional parameters in the presence of data truncation. Truncation distorts the likelihood surface to create instabilities and high variance in the estimation of these parameters, and the penalty terms help in many cases to decrease estimation error and increase robustness. Approximate methods are provided for choosing a priori good penalty estimators, which are shown to perform well in a series of simulation experiments. The robustness of the methods is explored heuristically using both simulated and real data drawn from an operational risk context. [ABSTRACT FROM AUTHOR]

Abstract: This article introduces a parametric robust way of determining the mean–variance relationship in the setting of generalized linear models. More specifically, the normal likelihood is properly amended to become asymptotically valid even if normality fails. Consequently, legitimate inference for the parametric relationship between mean and variance could be derived under model misspecification. More details are given to the scenario when the variance is proportional to an unknown power of the mean function. The efficacy of the novel technique is demonstrated via simulations and the analysis of two real data sets. [Copyright &y& Elsevier]

Abstract: The Forward Search is a powerful general method, incorporating flexible data-driven trimming, for the detection of outliers and unsuspected structure in data and so for building robust models. Starting from small subsets of data, observations that are close to the fitted model are added to the observations used in parameter estimation. As this subset grows we monitor parameter estimates, test statistics and measures of fit such as residuals. The paper surveys theoretical development in work on the Forward Search over the last decade. The main illustration is a regression example with 330 observations and 9 potential explanatory variables. Mention is also made of procedures for multivariate data, including clustering, time series analysis and fraud detection. [Copyright &y& Elsevier]

Abstract: In this paper we propose a method for correctly detecting outliers based on a new technique developed to simultaneously evaluate mean, variance and outliers. This method is capable of self-regulating its robustness to suit the experimental data set under analysis, so as to overcome shortcomings of: (i) nonrobust methods such as the least sum of squares; (ii) the need of the user in defining a trimmed sub-set of experimental points such as in least trimmed sum of squares; and (iii) the possibility to read the data set only once to evaluate the mean, variance, and outliers of a population by preserving robustness. [ABSTRACT FROM AUTHOR]