Subjects -> STATISTICS (Total: 130 journals)
 Showing 1 - 151 of 151 Journals sorted alphabetically Advances in Complex Systems       (Followers: 11) Advances in Data Analysis and Classification       (Followers: 61) Annals of Applied Statistics       (Followers: 39) Applied Categorical Structures       (Followers: 4) Argumentation et analyse du discours       (Followers: 11) Asian Journal of Mathematics & Statistics       (Followers: 8) AStA Advances in Statistical Analysis       (Followers: 4) Australian & New Zealand Journal of Statistics       (Followers: 13) Bernoulli       (Followers: 9) Biometrical Journal       (Followers: 11) Biometrics       (Followers: 52) British Journal of Mathematical and Statistical Psychology       (Followers: 18) Building Simulation       (Followers: 2) Bulletin of Statistics       (Followers: 4) CHANCE       (Followers: 5) Communications in Statistics - Simulation and Computation       (Followers: 9) Communications in Statistics - Theory and Methods       (Followers: 11) Computational Statistics       (Followers: 14) Computational Statistics & Data Analysis       (Followers: 37) Current Research in Biostatistics       (Followers: 8) Decisions in Economics and Finance       (Followers: 11) Demographic Research       (Followers: 15) Electronic Journal of Statistics       (Followers: 8) Engineering With Computers       (Followers: 5) Environmental and Ecological Statistics       (Followers: 7) ESAIM: Probability and Statistics       (Followers: 5) Extremes       (Followers: 2) Fuzzy Optimization and Decision Making       (Followers: 9) Geneva Papers on Risk and Insurance - Issues and Practice       (Followers: 13) Handbook of Numerical Analysis       (Followers: 5) Handbook of Statistics       (Followers: 7) IEA World Energy Statistics and Balances -       (Followers: 2) International Journal of Computational Economics and Econometrics       (Followers: 6) International Journal of Quality, Statistics, and Reliability       (Followers: 17) International Journal of Stochastic Analysis       (Followers: 3) International Statistical Review       (Followers: 13) International Trade by Commodity Statistics - Statistiques du commerce international par produit Journal of Algebraic Combinatorics       (Followers: 4) Journal of Applied Statistics       (Followers: 21) Journal of Biopharmaceutical Statistics       (Followers: 21) Journal of Business & Economic Statistics       (Followers: 39, SJR: 3.664, CiteScore: 2) Journal of Combinatorial Optimization       (Followers: 7) Journal of Computational & Graphical Statistics       (Followers: 20) Journal of Econometrics       (Followers: 84) Journal of Educational and Behavioral Statistics       (Followers: 6) Journal of Forecasting       (Followers: 17) Journal of Global Optimization       (Followers: 7) Journal of Interactive Marketing       (Followers: 10) Journal of Mathematics and Statistics       (Followers: 8) Journal of Nonparametric Statistics       (Followers: 6) Journal of Probability and Statistics       (Followers: 10) Journal of Risk and Uncertainty       (Followers: 33) Journal of Statistical and Econometric Methods       (Followers: 5) Journal of Statistical Physics       (Followers: 13) Journal of Statistical Planning and Inference       (Followers: 8) Journal of Statistical Software       (Followers: 21, SJR: 13.802, CiteScore: 16) Journal of the American Statistical Association       (Followers: 72, SJR: 3.746, CiteScore: 2) Journal of the Korean Statistical Society       (Followers: 1) Journal of the Royal Statistical Society Series C (Applied Statistics)       (Followers: 33) Journal of the Royal Statistical Society, Series A (Statistics in Society)       (Followers: 27) Journal of the Royal Statistical Society, Series B (Statistical Methodology)       (Followers: 43) Journal of Theoretical Probability       (Followers: 3) Journal of Time Series Analysis       (Followers: 16) Journal of Urbanism: International Research on Placemaking and Urban Sustainability       (Followers: 30) Law, Probability and Risk       (Followers: 8) Lifetime Data Analysis       (Followers: 7) Mathematical Methods of Statistics       (Followers: 4) Measurement Interdisciplinary Research and Perspectives       (Followers: 1) Metrika       (Followers: 4) Modelling of Mechanical Systems       (Followers: 1) Monte Carlo Methods and Applications       (Followers: 6) Monthly Statistics of International Trade - Statistiques mensuelles du commerce international       (Followers: 2) Multivariate Behavioral Research       (Followers: 5) Optimization Letters       (Followers: 2) Optimization Methods and Software       (Followers: 8) Oxford Bulletin of Economics and Statistics       (Followers: 34) Pharmaceutical Statistics       (Followers: 17) Probability Surveys       (Followers: 4) Queueing Systems       (Followers: 7) Research Synthesis Methods       (Followers: 8) Review of Economics and Statistics       (Followers: 128) Review of Socionetwork Strategies Risk Management       (Followers: 15) Sankhya A       (Followers: 2) Scandinavian Journal of Statistics       (Followers: 9) Sequential Analysis: Design Methods and Applications Significance       (Followers: 7) Sociological Methods & Research       (Followers: 38) SourceOCDE Comptes nationaux et Statistiques retrospectives SourceOCDE Statistiques : Sources et methodes SourceOECD Bank Profitability Statistics - SourceOCDE Rentabilite des banques       (Followers: 1) SourceOECD Insurance Statistics - SourceOCDE Statistiques d'assurance       (Followers: 2) SourceOECD Main Economic Indicators - SourceOCDE Principaux indicateurs economiques       (Followers: 1) SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques SourceOECD Monthly Statistics of International Trade       (Followers: 1) SourceOECD National Accounts & Historical Statistics SourceOECD OECD Economic Outlook Database - SourceOCDE Statistiques des Perspectives economiques de l'OCDE       (Followers: 2) SourceOECD Science and Technology Statistics - SourceOCDE Base de donnees des sciences et de la technologie SourceOECD Statistics Sources & Methods       (Followers: 1) SourceOECD Taxing Wages Statistics - SourceOCDE Statistiques des impots sur les salaires Stata Journal       (Followers: 9) Statistica Neerlandica       (Followers: 1) Statistical Applications in Genetics and Molecular Biology       (Followers: 5) Statistical Communications in Infectious Diseases Statistical Inference for Stochastic Processes       (Followers: 3) Statistical Methodology       (Followers: 7) Statistical Methods and Applications       (Followers: 6) Statistical Methods in Medical Research       (Followers: 27) Statistical Modelling       (Followers: 19) Statistical Papers       (Followers: 4) Statistical Science       (Followers: 13) Statistics & Probability Letters       (Followers: 13) Statistics & Risk Modeling       (Followers: 3) Statistics and Computing       (Followers: 13) Statistics and Economics       (Followers: 1) Statistics in Medicine       (Followers: 198) Statistics, Politics and Policy       (Followers: 6) Statistics: A Journal of Theoretical and Applied Statistics       (Followers: 15) Stochastic Models       (Followers: 3) Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports       (Followers: 2) Structural and Multidisciplinary Optimization       (Followers: 12) Teaching Statistics       (Followers: 7) Technology Innovations in Statistics Education (TISE)       (Followers: 2) TEST       (Followers: 3) The American Statistician       (Followers: 23) The Annals of Applied Probability       (Followers: 8) The Annals of Probability       (Followers: 10) The Annals of Statistics       (Followers: 34) The Canadian Journal of Statistics / La Revue Canadienne de Statistique       (Followers: 11) Wiley Interdisciplinary Reviews - Computational Statistics       (Followers: 1)
Similar Journals
 Fuzzy Optimization and Decision MakingJournal Prestige (SJR): 0.878 Citation Impact (citeScore): 3Number of Followers: 9      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1573-2908 - ISSN (Online) 1568-4539 Published by Springer-Verlag  [2657 journals]
• Uncertain growth model for the cumulative number of COVID-19 infections in
China
• Abstract: As a type of coronavirus, COVID-19 has quickly spread around the majority of countries worldwide, and seriously threatens human health and security. This paper aims to depict cumulative numbers of COVID-19 infections in China using the growth model chosen by cross validation. The residual plot does not look like a null plot, so we can not find a distribution function for the disturbance term that is close enough to the true frequency. Therefore, the disturbance term can not be characterized as random variables, and stochastic regression analysis is invalid in this case. To better describe this pandemic automatically, this paper first employs uncertain growth models with the help of uncertain hypothesis tests to detect and modify outliers in data. The forecast value and confidence interval for the cumulative number of COVID-19 infections in China are provided.
PubDate: 2021-06-01

• Analysis and prediction of confirmed COVID-19 cases in China with
uncertain time series
• Abstract: This paper presents an uncertain time series model to analyse and predict the evolution of confirmed COVID-19 cases in China, excluding imported cases. Compared with the results of the classical time series model, the uncertain time series model could better describe the COVID-19 epidemic by using an uncertain hypothesis test to filter out outliers. This improvement is reflected in the two observations. One is that the estimated variance of the disturbance term in the uncertain time series model is more appropriate and acceptable than that in the classical time series model, and the other is that the disturbance term of the classical time series model cannot be regarded as a random variable but as an uncertain variable.
PubDate: 2021-06-01

• Uncertain SEIAR model for COVID-19 cases in China
• Abstract: The Susceptible-Exposed-Infectious-Asymptomatic-Removed (SEIAR) epidemic model is one of most frequently used epidemic models. As an application of uncertain differential equations to epidemiology, an uncertain SEIAR model is derived which considers the human uncertainty factors during the spread of an epidemic. The parameters in the uncertain epidemic model are estimated with the numbers of COVID-19 cases in China, and a prediction to the possible numbers of active cases is made based on the estimates.
PubDate: 2021-06-01

• Initial value estimation of uncertain differential equations and zero-day
• Abstract: Assume an uncertain process follows an uncertain differential equation, and some realizations of this process are observed. Parameter estimation for the uncertain differential equation that fits the observed data as much as possible is a core problem in practice. This paper first presents a problem of initial value estimation for uncertain differential equations and proposes an estimation method. In addition, the method of moments is recast for estimating the time-varying parameters in uncertain differential equations. Using those techniques, a COVID-19 spread model based on uncertain differential equation is derived, and the zero-day of COVID-19 spread in China is inferred.
PubDate: 2021-06-01

• Numerical solution and parameter estimation for uncertain SIR model with
application to COVID-19
• Abstract: Developing algorithms for solving high-dimensional uncertain differential equations has been an exceedingly difficult task. This paper presents an $$\alpha$$ -path-based approach that can handle the proposed high-dimensional uncertain SIR model. We apply the $$\alpha$$ -path-based approach to calculating the uncertainty distributions and related expected values of the solutions. Furthermore, we employ the method of moments to estimate parameters and design a numerical algorithm to solve them. This model is applied to describing the development trend of COVID-19 using infected and recovered data of Hubei province. The results indicate that lockdown policy achieves almost 100% efficiency after February 13, 2020, which is consistent with the existing literatures. The high-dimensional $$\alpha$$ -path-based approach opens up new possibilities in solving high-dimensional uncertain differential equations and new applications.
PubDate: 2021-06-01

• A relation between moments of Liu process and Bernoulli numbers
• Abstract: This paper finds a relation between moments of Liu process and Bernoulli numbers. Firstly, by an exponential generating function of Bernoulli numbers, a useful integral formula is obtained. Secondly, based on this integral formula, the moments of a normal uncertain variable and Liu process are expressed via Bernoulli numbers.
PubDate: 2021-06-01

• Editor’s message
• PubDate: 2021-04-29

• The DEMATEL–COPRAS hybrid method under probabilistic linguistic
environment and its application in Third Party Logistics provider
selection
• Abstract: With the emergence of outsourcing logistics and the rapid development of the e-commerce business, Third Party Logistics (TPL) plays an indispensable role in modern business. In the TPL provider selection process, uncertain information brings more challenges to decision makers. This paper uses probabilistic linguistic term sets (PLTSs) to describe uncertain decision making information. Firstly, we propose an improved Decision Making Trial and Evaluation Laboratory method, which allows a certain relationship between decision criteria and calculates criteria weights in multi-criteria decision making (MCDM) problems. Then, in order to make full use of uncertain TPL provider information and maximize the values of data, the probabilistic linguistic complex proportional assessment method is proposed and applied to solve the MCDM problems under probabilistic linguistic environment, which needs much less computation than other MCDM methods. Finally, an application example of TPL provider selection is presented to demonstrate the proposed method. A comparative analysis is further conducted to validate the effectiveness of the proposed method.
PubDate: 2021-04-26

• A comparative analysis of probabilistic linguistic preference relations
and distributed preference relations for decision making
• Abstract: When a decision-maker prefers to compare different alternatives in pairs to handle real situations, there are many different expression styles that can be used. Two representative expression styles are the probabilistic linguistic preference relation (PLPR), which originates from the fuzzy linguistic approach and the distributed preference relation (DPR), which originates from the evidential reasoning approach. Although these two expression styles look quite similar, their meanings, operations, and relevant decision making processes are significantly different. This presents the decision-maker with the challenge of selecting either PLPRs or DPRs in different real cases. To address this issue, this paper provides a detailed analysis of the similarities and differences between PLPRs and DPRs. The analysis is conducted from five perspectives, including modeling of decision making problems, handling of uncertainty, consistency between preference relations, information aggregation, and elicitation process. An engineer selection problem for an automobile manufacturing enterprise is investigated to demonstrate how to appropriately select PLPRs or DPRs to model and analyze decision making problems in real situations with consideration for the preferences of decision-makers.
PubDate: 2021-04-20

• Common probability-based interactive algorithms for group decision making
with normalized probability linguistic preference relations
• Abstract: Probabilistic linguistic variable is a kind of powerful qualitative fuzzy sets, which permits the decision makers (DMs) to apply several linguistic variables with probabilities to denote a judgment. This paper studies group decision making (GDM) with normalized probability linguistic preference relations (NPLPRs). To achieve this goal, an acceptably multiplicative consistency based interactive algorithm is provided to derive common probability linguistic preference relations (CPLPRs) from PLPRs, by which a new acceptably multiplicative consistency concept for NPLPRs is defined. When the multiplicative consistency of NPLPRs is unacceptable, models for deriving acceptably multiplicatively consistent NPLPRs are constructed. Then, it studies incomplete NPLPRs (InNPLPRs) and offers a common probability and acceptably multiplicative consistency based interactive algorithm to determine missing judgments. Furthermore, a correlation coefficient between CPLPRs is provided, by which the weights of the DMs are ascertained. Meanwhile, a consensus index based on CPLPRs is defined. When the consensus does not reach the requirement, a model to increase the level of consensus is built that can ensure the adjusted LPRs to meet the multiplicative consistency and consensus requirement. Moreover, an interactive algorithm for GDM with NPLPRs is provided, which can address unacceptably multiplicatively consistent InNPLPRs. Finally, an example about the evaluation of green design schemes for new energy vehicles is provided to indicate the application of the new algorithm and comparative analysis is conducted.
PubDate: 2021-04-15

• Selecting products through text reviews: An MCDM method incorporating
personalized heuristic judgments in the prospect theory
• Abstract: Online reviews have become an increasingly popular information source in consumer’s decision making process. To help consumers make informed decisions, how to select products through online reviews is a valuable research topic. This work deals with a personized product selection problem with review sentiments under probabilistic linguistic circumstances. To this end, we propose a multi-criteria decision making (MCDM) method incorporating personalized heuristic judgments in the prospect theory (PT). We focus on the role of personalized heuristic judgments on review helpfulness in the final decision outcomes. We demonstrate the consistency between the three common heuristic judgments (with respect to review valence, sentiment extremity, and aspiration levels) and the three behavioral principles of the PT. Then, the products are ranked with the probabilistic linguistic term set (PLTS) input, based on the proposed adjustable PT framework, in which the coefficients of negativity bias are derived from the consumer’s heuristic judgments. Finally, a real case on TripAdvisor.com and two simulation experiments are given to illustrate the validity of the proposed method.
PubDate: 2021-04-15

• An approach for solving fuzzy multi-criteria decision problem under
linguistic information
• Abstract: Linguistic information processing exists in multi-criteria decision making, and linguistic truth-valued lattice implication algebra (LTV-LIA) has definite advantages in handling comparable and incomparable linguistic values. To deal with the preference relations with linguistic evaluation information, we establish a novel approach for solving fuzzy multi-criteria decision problem under linguistic information based on LTV-LIA. In this paper, we propose linguistic lattice-valued preference relation (LLVPR). LLVPR positive and negative matrixes are introduced to evaluate the advantages and disadvantages of alternatives respectively. In order to get a reasonable result, we introduce a new algorithm to check and repair the consistency of a LLVPR. A linguistic lattice-valued 2-tuple representation model (LLV2-tuple) and some new aggregation operations are presented to get the comprehensive linguistic information without information loss. Considering different decision makers have different preferences, a multiple preferences implication operation of LLV2-tuple is introduced. Finally, we propose a novel linguistic analytic hierarchy process embedded in aggregation layer and implication layer, introducing algorithm and numerical examples. A comparative analysis is adopted to illustrate the rationality.
PubDate: 2021-04-07

• Uncertain random data envelopment analysis for technical efficiency
• Abstract: Data envelopment analysis (DEA) is a classical and prevailing tool for estimating relative efficiencies of multiple decision making units (DMUs). However, sometimes DMUs’ inputs and outputs cannot be observed accurately in practical cases, and hence this paper attempts to propose an uncertain random DEA model to evaluate the efficiencies of DMUs with uncertain random inputs and outputs. The sensitivity and stability of this new model are further analyzed with the aim to figure out the stability radius of each DMU. Finally, a numerical example is presented for illustrating the proposed uncertain random DEA model.
PubDate: 2021-04-05

• Incremental maintenance of discovered fuzzy association rules
• Abstract: Fuzzy association rules (FARs) are a recognized model to study existing relations among data, commonly stored in data repositories. In real-world applications, transactions are continuously processed with upcoming new data, rendering the discovered rules information inexact or obsolete in a short time. Incremental mining methods arise to avoid re-runs of those algorithms from scratch by re-using information that is systematically maintained. These methods are useful for extracting knowledge in dynamic environments. However, executing the algorithms only to maintain previously discovered information creates inefficiencies in real-time decision support systems. In this paper, two active algorithms are proposed for incremental maintenance of previously discovered FARs, inspired by efficient methods for change computation. The application of a generic form of measures in these algorithms allows the maintenance of a wide number of metrics simultaneously. We also propose to compute data operations in real-time, in order to create a reduced relevant instance set. The algorithms presented do not discover new knowledge; they are just created to efficiently maintain valuable information previously extracted, ready for decision making. Experimental results on education data and repository data sets show that our methods achieve a good performance. In fact, they can significantly improve traditional mining, incremental mining, and a naïve approach.
PubDate: 2021-03-31

• Z probabilistic linguistic term sets and its application in
multi-attribute group decision making
• Abstract: Probabilistic linguistic term set solves the problem of probabilistic distribution of linguistic terms. Due to the objective and subjective factors such as the decision makers’experience and preference, the credibility of the linguistic terms is different. However, current studies on PLTSs ignore this difference. In this paper, we first propose a novel concept called Z probabilistic linguistic term set (ZPLTS). As an extension of existing tools, it takes advantage of the fact that Z-number can represent both information and corresponding credibility. At the same time, we discuss the normalization, operational rules, ranking method and distance measure for ZPLTSs. Then, we propose a new weight calculation method, an aggregation-based method and an extended TOPSIS method, and apply them to multi-attribute group decision making in Z probabilistic linguistic environment. Finally, a numerical example and some comparisons with other methods illustrate the necessity and effectiveness of the proposed method.
PubDate: 2021-03-13

• Option pricing formulas based on uncertain fractional differential
equation
• Abstract: Uncertain fractional differential equations have been playing an important role in modelling complex dynamic systems. Early researchers have presented the extreme value theorems and time integral theorem on uncertain fractional differential equation. As applications of these theorems, this paper investigates the pricing problems of American option and Asian option under uncertain financial markets based on uncertain fractional differential equations. Then the analytical solutions and numerical solutions of these option prices are derived, respectively. Finally, some numerical experiments are performed to verify the effectiveness of our results.
PubDate: 2021-03-05
DOI: 10.1007/s10700-021-09354-z

• Managing consensus reaching process with self-confident double hierarchy
linguistic preference relations in group decision making
• Abstract: Group decision making (GDM) can be defined as an environment where there exist a set of possible alternatives and a set of individuals (experts, judgements, etc.). Preference relation is one of the most widely used preference representation structures in GDM. Considering that the self-confidence degree is an important part to express preference information, and double hierarchy linguistic preference relation (DHLPR) is a cognitive complex linguistic information representation tool to express complex linguistic information, this paper presents a novel preference relation named as self-confident DHLPR. In addition, a weight-determining method is developed, which considers three kinds of information including the subjective weights and two kinds of objective weights. Furthermore, a consensus model is set up to manage the GDM problems with self-confident DHLPRs based on the priority ordering theory. The effectiveness of the proposed consensus model is illustrated by a case study concerning the selection of optimal hospitals in the field of Telemedicine. Finally, a simulation experiment is devised to testify the proposed consensus model and then some comparisons with other consensus reaching models are provided from three different angles including the number of iterations, the consensus success ratio and the distance between the original and adjusted preferences.
PubDate: 2021-03-01
DOI: 10.1007/s10700-020-09331-y

• Stability analysis for uncertain differential equation by Lyapunov’s
second method
• Abstract: Uncertain differential equation is a type of differential equation driven by Liu process that is the counterpart of Wiener process in the framework of uncertainty theory. The stability theory is of particular interest among the properties of the solutions to uncertain differential equations. In this paper, we introduce the Lyapunov’s second method to study stability in measure and asymptotic stability of uncertain differential equation. Different from the existing results, we present two sufficient conditions in sense of Lyapunov stability, where the strong Lipschitz condition of the drift is no longer indispensable. Finally, illustrative examples are examined to certify the effectiveness of our theoretical findings.
PubDate: 2021-03-01
DOI: 10.1007/s10700-020-09336-7

• A random intuitionistic fuzzy factor analysis model for complex
multi-attribute large group decision-making in dynamic environments
• Abstract: The challenge of complex multi-attribute large group decision-making (CMALGDM) is reflected from three perspectives: interrelated attributes, large group decision makers (DMs) and dynamic decision environments. However, there are few decision techniques that can address the three perspectives simultaneously. This paper proposes a random intuitionistic fuzzy factor analysis model, aiming to address the challenge of CMALGDM from the three perspectives. The proposed method effectively reduces the dimensionality of the original data and takes into account the underlying random environmental factors which may affect the performances of alternatives. The development of this method follows three steps. First, the random intuitionistic fuzzy variables are developed to deal with a hybrid uncertain situation where fuzziness and randomness co-exist. Second, a novel factor analysis model for random intuitionistic fuzzy variables is proposed. This model uses specific mappings or functions to define the way in which evaluations are affected by the dynamic environment vector through data learning or prior distributions. Third, multiple correlated attribute variables and DM variables are transformed into fewer independent factors by a two-step procedure using the proposed model. In addition, the objective classifications and weights for attributes and DMs are obtained from the results of orthogonal rotated factor loading. An illustrative case and detailed comparisons of decision results in different environmental conditions are demonstrated to test the feasibility and validity of the proposed method.
PubDate: 2021-03-01
DOI: 10.1007/s10700-020-09334-9

• Towards fuzzy anomaly detection-based security: a comprehensive review
• Abstract: In the data security context, anomaly detection is a branch of intrusion detection that can detect emerging intrusions and security attacks. A number of anomaly detection systems (ADSs) have been proposed in the literature that using various algorithms and techniques try to detect the intrusions and anomalies. This paper focuses on the ADS schemes which have applied fuzzy logic in combination with other machine learning and data mining techniques to deal with the inherent uncertainty in the intrusion detection process. For this purpose, it first presents the key knowledge about intrusion detection systems and then classifies the fuzzy ADS approaches regarding their utilized fuzzy algorithm. Afterward, it summarizes their major contributions and illuminates their advantages and limitations. Finally, concluding issues and directions for future researches in the fuzzy ADS context are highlighted.
PubDate: 2021-03-01
DOI: 10.1007/s10700-020-09332-x

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