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This paper is to propose a novel generalized grey target decision method (GGTDM) with index and weight both containing mixed types of data.

The decision-making steps of the proposed approach are as follows. First, all mixed attribute values of alternatives and weights are transformed into binary connection numbers and also comprised two-tuple (determinacy, uncertainty) numbers. Then, the two-tuple (determinacy, uncertainty) numbers of target center indices are calculated. Next, the certain weights are determined by the Gini–Simpson (G–S) index-based method. Following this, the comprehensive-weighted Kullback–Leibler distances (CWKLDs) of all alternatives and the target center are obtained. Finally, the alternative ranking relies on the CWKLD considering the smaller value as the better option.

The certain weights determined by the improved Gini–Simpson index (IGSI) based method are more accurate in compared with that by the proximity-based method and the weight function method. The discrimination ability of alternatives ranking of the proposed approach is stronger than that of the compared comprehensive-weighted proximity (CWP) based method and comprehensive-weighted Gini–Simpson index (CWGSI) based method.

The proposed method fulfills the decision-making task relying on CWKLD, which solves the uncertain measurement from the viewpoint of entropy.

The proposed approach adopts the IGSI to transform uncertain weights into certain ones and takes the CWKLD as the basis for the decision-making.

In many problems involving decision‐making under uncertainty, the underlying probability model is unknown but partial information is available. In some approaches to this problem, the available prior information is used to define an appropriate probability model for the system uncertainty through a probability density function. When the prior information is available as a finite sequence of moments of the unknown probability density function (PDF) defining the appropriate probability model for the uncertain system, the maximum entropy (ME) method derives a PDF from an exponential family to define an approximate model. This paper, aims to investigate some optimality properties of the ME estimates.

For n>m, when the exact model can be best approximated by one of an infinite number of unknown PDFs from an n parameter exponential family. The upper bound of the divergence distance between any PDF from this family and the m parameter exponential family PDF defined by the ME method are derived. A measure of adequacy of the model defined by ME method is thus provided.

These results may be used to establish confidence intervals on the estimate of a function of the random variable when the ME approach is employed. Additionally, it is shown that when working with large samples of independent observations, a probability density function (PDF) can be defined from an exponential family to model the uncertainty of the underlying system with measurable accuracy. Finally, a relationship with maximum likelihood estimation for this case is established.

The so‐called known moments problem addressed in this paper has a variety of applications in learning, blind equalization and neural networks.

An upper bound for error in approximating an unknown density function, f(x) by its ME estimate based on m moment constraints, obtained as a PDF p(x, α) from an m parameter exponential family is derived. The error bound will help us decide if the number of moment constraints is adequate for modeling the uncertainty in the system under study. In turn, this allows one to establish confidence intervals on an estimate of some function of the random variable, X, given the known moments. It is also shown how, when working with a large sample of independent observations, instead of precisely known moment constraints, a density from an exponential family to model the uncertainty of the underlying system with measurable accuracy can be defined. In this case, a relationship to ML estimation is established.

The purpose of this paper is to investigate a novel generalised grey target decision method (GGTDM) with index and weight involving mixed attribute values.

The mixed attribute values are transformed into binary connection numbers and also comprised of two-tuple (determinacy, uncertainty) numbers to fulfil the decision-making task. The proposed method constructs the weight function to convert the mixed attribute-based weights into the certain number-based weights and determines the alternatives ranking by the comprehensive weighted Gini–Simpson indices (CWGSIs).

The result of decision making regarding the numerical example by the proposed approach is somewhat different from that obtained by the reported vector-based method. The reasons for this are threefold: the decision-making bases are different, the target centre indices are determined by different mechanisms and certain number-based weights are calculated in different ways.

The proposed method ranks an alternative based on the Gini–Simpson index, as derived from the viewpoint of measuring the uncertainty (heterogeneity): however, the vector-based GGTDM makes a decision based on proximity, as is the case when measuring the similarities between index vectors.

The proposed approach is admissible to solving mixed attribute-based decision making especially for alternative indices and attribute weights containing both uncertain numbers.

The proposed method provides a new perspective on measuring the difference of alternatives to the target centre via the CWGSI: the CWGSI is obtained by relying on the pseudo-probabilities achieved by the ratios of the alternative indices to the target centre indices. It also builds a weight function converting the mixed attribute-based weights into certain number-based weights. This method provides a framework that should be tested in terms of its effective decision making using real data and an actual problem.

There is great uncertainty and volatility in the evaluation and measurement of green supplier satisfaction. The purpose of this paper is to fill this gap based on the information entropy theory (IET) to describe the probability of green supplier satisfaction degree.

The authors introduce a formal model using analytic hierarchy process (AHP), IET and entropy technique for order preference by similarity to an ideal solution (TOPSIS) method to evaluate green supplier satisfaction and promote them for the better implementation of green supply chain management practices.

The first finding is developing an effective framework for green supplier satisfaction, incorporating various measures of environmental dimension. Second, a hybrid uncertainty decision method is introduced, by integrating AHP and IET and entropy-TOPSIS.

One of the main limitations of the research is that the authors introduced a conceptual example. Real-world applications need to investigate the accuracy and effectiveness of these measures, and the operational feasibility of this method.

This is one of the first works to provide a comprehensive appraisal model for evaluation of green supplier satisfaction. This study and research method can form general guidelines, and organizations can increasingly benefit from using green supplier satisfaction evaluation as a management tool. Green supplier satisfaction evaluation is just the beginning.

This chapter focuses on an important aspect of economic inequality – the question of how people perceive inequality and whether these perceptions deviate in any meaningful way from statistical measures of inequality. Using a novel approach, the author investigates whether individuals across different countries are able to correctly estimate the shape of income distribution of the country where they reside. The author further investigates whether individuals have the distribution of a particular reference group in mind when they answer questions on inequality. The author finds that perceptions of inequality are frequently shaped by reference groups such as those formed according to educational attainment, age, and gender.

Large amount of data are stored in image format. Image retrieval from bulk databases has become a hot research topic. An alternative method for efficient image retrieval is proposed based on a combination of texture and colour information. The main purpose of this paper is to propose a new content based image retrieval approach using combination of color and texture information in spatial and transform domains jointly.

Various methods are provided for image retrieval, which try to extract the image contents based on texture, colour and shape. The proposed image retrieval method extracts global and local texture and colour information in two spatial and frequency domains. In this way, image is filtered by Gaussian filter, then co-occurrence matrices are made in different directions and the statistical features are extracted. The purpose of this phase is to extract noise-resistant local textures. Then the quantised histogram is produced to extract global colour information in the spatial domain. Also, Gabor filter banks are used to extract local texture features in the frequency domain. After concatenating the extracted features and using the normalised Euclidean criterion, retrieval is performed.

The performance of the proposed method is evaluated based on the precision, recall and run time measures on the Simplicity database. It is compared with many efficient methods of this field. The comparison results showed that the proposed method provides higher precision than many existing methods.

The comparison results showed that the proposed method provides higher precision than many existing methods. Rotation invariant, scale invariant and low sensitivity to noise are some advantages of the proposed method. The run time of the proposed method is within the usual time frame of algorithms in this domain, which indicates that the proposed method can be used online.

We investigate the Bayesian approach to model comparison within a two-country framework with nominal rigidities using the workhorse New Keynesian open-economy model of Martínez-García and Wynne (2010). We discuss the trade-offs that monetary policy – characterized by a Taylor-type rule – faces in an interconnected world, with perfectly flexible exchange rates. We then use posterior model probabilities to evaluate the weight of evidence in support of such a model when estimated against more parsimonious specifications that either abstract from monetary frictions or assume autarky by means of controlled experiments that employ simulated data. We argue that Bayesian model comparison with posterior odds is sensitive to sample size and the choice of observable variables for estimation. We show that posterior model probabilities strongly penalize overfitting, which can lead us to favor a less parameterized model against the true data-generating process when the two become arbitrarily close to each other. We also illustrate that the spillovers from monetary policy across countries have an added confounding effect.

This study examines the prediction power of investor sentiment on Bitcoin return.

We construct a Financial and Economic Attitudes Revealed by Search (FEARS) index using search volume from Google's search engine to reveal household-level (“bankruptcy”, “unemployment”, “job search”, etc.) and market-level sentiment (“bankruptcy”, “unemployment”, “job search”, etc.).

Using a variety of quantitative methodologies such as the transfer entropy model as well as threshold regression and OLS, GLS and 2SLS estimations, we find that (1) investor sentiment has strong predictive power on Bitcoin, (2) household-level sentiment has larger effects than market-level sentiment and (3) the impact of sentiment is greater in low sentiment regimes than in high sentiment regimes. Based on these information, we build a hypothetical trading strategy that outperforms a simple buy-and-hold strategy both on an absolute and risk-adjusted basis. The results are consistent across cryptocurrencies and regions.

The findings contribute to the ongoing debate in the literature on the efficiency of cryptocurrency markets. The results reveal that the Bitcoin market is not efficient in the sense of the efficient market hypothesis – asset prices do not fully reflect all available information and we were able to “beat the market”. In addition, it sheds further light on the debate whether Bitcoin can be considered a medium of exchange, i.e. a currency or an investment product. Because investors are reallocating their Bitcoin holdings during times of increased market sentiment due to liquidity needs, they obviously consider bitcoin an investment product rather than a currency.

This study is the first to examine the impact of investor sentiment measured by FEARS on Bitcoin return.

This study extends the educational institutions' performance and efficiency literature by examining Brazil's Federal Institute of Education, Science and Technology (FIEST), which consists of educational units throughout the country that span several levels of education.

The authors build and analyze a covariance matrix consisting of both a group of efficiency measures and a group of performance indicators used by Brazil's Ministry of Education (BME). The values in the covariance matrix are maximized through application of the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS), in which the weights of each variable are optimized in order to capture the direction of the relationship between the two sets of efficiency measures.

Although the authors find that the collective efficiency of the educational units analyzed did not change during the period of study, the analysis reveals that government indicators of performance do not exhibit a strong relationship to the ideal solution efficiency measures used in this study.

This study extends the educational institution efficiency literature by examining Brazil's FIEST, which consists of 40 educational units throughout the country that spans several levels of education, from upper high school vocational courses to higher degrees.