Types Of Data Uncertainty Research Articles

Структурные сдвиги и тестирование на единичный корень (Structural Breaks and Unit Root Testing

Russian Abstract: В данном обзоре рассматриваются методы тестирования наличия единичного корня во временном ряде при наличии структурных сдвигов. Отдельно обсуждаются методы оценивания дат структурных сдвигов при неопределенности относительно того, является ли временной ряд стационарным или нет. Обзор покрывает большое количество недавно разработанных методов тестирования, робастных к различным типам неопределенности данных, включая начальные значения и изменяющуюся во времени волатильность. Обзор предоставляет читателю возможность практического применения современных методов тестирования в совокупности с пониманием системы развития этих методов. English Abstract: This review discusses methods of testing for a unit root in a time series in the presence of structural breaks. Separately the methods for break date estimation under uncertainty over the order of integration are considered. The review covers a large number of recently developed testing methods that are robust to various types of data uncertainty, including initial conditions and time-varying volatility. The review provides the reader the possibility of practical application of modern testing methods together with an understanding of the development of these methods.

Структурные сдвиги и тестирование на единичный корень

his review discusses methods of testing for a unit root in a time series in the presence of structural breaks. Separately the methods for break date estimation under uncertainty over the order of integration are considered. The review covers a large number of recently developed testing methods that are robust to various types of data uncertainty, including initial conditions and time-varying volatility. The survey introduces the reader to practical application of modern testing methods together with an understanding of the development of these methods.

PwAdaBoost: Possible world based AdaBoost algorithm for classifying uncertain data

Possible world has become one of the most effective tools to deal with various types of data uncertainty in uncertain data management. However, few uncertain data classification algorithms are proposed based on possible world. Most existing uncertain data classification algorithms are simply extended from traditional classification algorithms for certain data. They deal with data uncertainty based on relatively ideal probability distribution and data type assumptions, thus are difficult to be applied for various application scenarios. In this paper, we propose a novel possible world based AdaBoost algorithm for classifying uncertain data, called PwAdaBoost. In the training procedure, PwAdaBoost uses the possible world set generated from the uncertain training set sampled in each iteration to train the sub-basic classifiers, and employs the possible world set generated from the whole uncertain training set to adjust the weights of the sub-basic classifiers and detect the quality of the basic classifiers. In the prediction procedure, PwAdaBoost utilizes the possible world set generated from the predicted object to get the results of the basic classifiers via majority voting and weighted voting. Furthermore, we analyze the stability and give the parallelization strategies for its training procedure and prediction procedure respectively. The proposed PwAdaBoost can deal with various types of data uncertainty, and use any existing classification algorithms for certain data to serve for uncertain data. As far as we know, it is the first ensemble classification algorithm for uncertain data. Extensive experiment results demonstrate the superiority of our proposed algorithm in terms of effectiveness and efficiency.

Robust Maximum Likelihood Estimation

In many applications, statistical estimators serve to derive conclusions from data, for example, in finance, medical decision making, and clinical trials. However, the conclusions are typically dependent on uncertainties in the data. We use robust optimization principles to provide robust maximum likelihood estimators that are protected against data errors. Both types of input data errors are considered: (a) the adversarial type, modeled using the notion of uncertainty sets, and (b) the probabilistic type, modeled by distributions. We provide efficient local and global search algorithms to compute the robust estimators and discuss them in detail for the case of multivariate normally distributed data. The estimator performance is demonstrated on two applications. First, using computer simulations, we demonstrate that the proposed estimators are robust against both types of data uncertainty and provide more accurate estimates compared with classical estimators, which degrade significantly, when errors are encountered. We establish a range of uncertainty sizes for which robust estimators are superior. Second, we analyze deviations in cancer radiation therapy planning. Uncertainties among plans are caused by patients’ individual anatomies and the trial-and-error nature of the process. When analyzing a large set of past clinical treatment data, robust estimators lead to more reliable decisions when applied to a large set of past treatment plans.

A Novel Approach to Neutrosophic Soft Rough Set under Uncertainty

To handle indeterminate and incomplete data, neutrosophic logic/set/probability were established. The neutrosophic truth, falsehood and indeterminacy components exhibit symmetry as the truth and the falsehood look the same and behave in a symmetrical way with respect to the indeterminacy component which serves as a line of the symmetry. Soft set is a generic mathematical tool for dealing with uncertainty. Rough set is a new mathematical tool for dealing with vague, imprecise, inconsistent and uncertain knowledge in information systems. This paper introduces a new rough set model based on neutrosophic soft set to exploit simultaneously the advantages of rough sets and neutrosophic soft sets in order to handle all types of uncertainty in data. The idea of neutrosophic right neighborhood is utilised to define the concepts of neutrosophic soft rough (NSR) lower and upper approximations. Properties of suggested approximations are proposed and subsequently proven. Some of the NSR set concepts such as NSR-definability, NSR-relations and NSR-membership functions are suggested and illustrated with examples. Further, we demonstrate the feasibility of the newly rough set model with decision making problems involving neutrosophic soft set. Finally, a discussion on the features and limitations of the proposed model is provided.

Possible world based consistency learning model for clustering and classifying uncertain data

Possible world has shown to be effective for handling various types of data uncertainty in uncertain data management. However, few uncertain data clustering and classification algorithms are proposed based on possible world. Moreover, existing possible world based algorithms suffer from the following issues: (1) they deal with each possible world independently and ignore the consistency principle across different possible worlds; (2) they require the extra post-processing procedure to obtain the final result, which causes that the effectiveness highly relies on the post-processing method and the efficiency is also not very good. In this paper, we propose a novel possible world based consistency learning model for uncertain data, which can be extended both for clustering and classifying uncertain data. This model utilizes the consistency principle to learn a consensus affinity matrix for uncertain data, which can make full use of the information across different possible worlds and then improve the clustering and classification performance. Meanwhile, this model imposes a new rank constraint on the Laplacian matrix of the consensus affinity matrix, thereby ensuring that the number of connected components in the consensus affinity matrix is exactly equal to the number of classes. This also means that the clustering and classification results can be directly obtained without any post-processing procedure. Furthermore, for the clustering and classification tasks, we respectively derive the efficient optimization methods to solve the proposed model. Experimental results on real benchmark datasets and real world uncertain datasets show that the proposed model outperforms the state-of-the-art uncertain data clustering and classification algorithms in effectiveness and performs competitively in efficiency.

Identifying Critical Components of a Public Transit System for Outbreak Control

Modern public transport networks provide an efficient medium for the spread of infectious diseases within a region. The ability to identify components of the public transit system most likely to be carrying infected individuals during an outbreak is critical for public health authorities to be able to plan for outbreaks, and control their spread. In this study we propose a novel network structure, denoted as the vehicle trip network, to capture the dynamic public transit ridership patterns in a compact form, and illustrate how it can be used for efficient detection of the high risk network components. We evaluate a range of network-based statistics for the vehicle trip network, and validate their ability to identify the routes and individual vehicles most likely to spread infection using simulated epidemic scenarios. A variety of outbreak scenarios are simulated, which vary by their set of initially infected individuals and disease parameters. Results from a case study using the public transit network from Twin Cities, MN are presented. The results indicate that the set of transit vehicle trips at highest risk of infection can be efficiently identified, and are relatively robust to the initial conditions of the outbreak. Furthermore, the methods are illustrated to be robust to two types of data uncertainty, those being passenger infection levels and travel patterns of the passengers.

On the estimation of parameters, survival functions, and hazard rates based on fuzzy life time data

ABSTRACTLife time data analysis is regarded as one of the significant out-shoots of statistics. Classical statistical techniques reckon life time observations as precise numbers and solely cover variation among the observations. In fact, there are two types of uncertainty in data: variation among the observations and the fuzziness. To this effect, the analysis techniques, which do not consider fuzziness and are only based on precise life time observations, use incomplete information; hence lead to pseudo results. This study aimed at generalizing parameters estimation, survival functions, and hazard rates for fuzzy life time data.

Robust delay-constrained routing in telecommunications

In telecommunications, operators usually use market surveys and statistical models to estimate traffic evolution in networks or to approximate queuing delay functions in routing strategies. Many research activities concentrated on handling traffic uncertainty in network design. Measurements on real world networks have shown significant errors in delay approximations, leading to weak management decisions in network planning. In this work, we introduce elements of robust optimization theory for delay modeling in routing problems. Different types of data uncertainty are considered and linked to corresponding robust models.

Joint Treatment of Random Variability and Imprecision in GPS Data Analysis

In the geodetic applications of the Global Positioning System (GPS) various types of data uncertainty are relevant. The most prominent ones are random variability (stochasticity) and imprecision. Stochasticity is caused by uncontrollable effects during the observation process. Imprecision is due to remaining systematic deviations between data and model due to imperfect knowledge or just for practical reasons. Depending on the particular application either stochasticity or imprecision may dominate the uncertainty budget. For the joint treatment of stochasticity and imprecision two main problems have to be solved. First, the imprecision of the original data has to be modelled in an adequate way. Then this imprecision has to be transferred to the quantities of interest. Fuzzy data analysis offers a proper mathematical theory to handle both problems. The main outcome is confidence regions for estimated parameters which are superposed by the effects of data imprecision. In the paper two applications are considered in a general way: the resolution of the phase ambiguity parameters and the estimation of point positions. The paper concludes with numerical examples for ambiguity resolution.

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.