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The purpose of the paper is to introduce the Jensen–inaccuracy measure and examine its properties. Furthermore, some results on the connections between the inaccuracy and Jensen–inaccuracy measures and some other well-known information measures are provided. Moreover, in three different optimization problems, the arithmetic mixture distribution provides optimal information based on the inaccuracy information measure. Finally, two real examples from image processing are studied and some numerical results in terms of the inaccuracy and Jensen–inaccuracy information measures are obtained.

To overcome the drawbacks of Shannon’s entropy, the concept of cumulative residual and past entropy has been proposed in the information theoretic literature. Furthermore, the Shannon entropy has been generalized in a number of different ways by many researchers. One important extension is Kerridge inaccuracy measure. In the present communication we study the cumulative residual and past inaccuracy measures, which are extensions of the corresponding cumulative entropies. Several properties, including monotonicity and bounds, are obtained for left, right and doubly truncated random variables.

Concerning the notorious Sleeping Beauty problem, philosophers have debated whether 1/2 or 1/3 is rational as Beauty’s credence in (H) the coin’s landing heads. According to Kierland and Monton, the answer depends on whether her goal is to minimize average or total inaccuracy because, while the expected average inaccuracy of Halfing (i.e., assigning 1/2 to H) is smaller than that of Thirding (i.e., assigning 1/3 to H), the expected total inaccuracy of Thirding is lower than that of Halfing. In this paper, I argue that Halfing is average accuracy dominated but Thirding is not; and that each of the standard forms of Halfing and Thirding regards a different credence assignment as better than itself in terms of total accuracy. Therefore, Halfing is irrational, and Thirding is likely to be rational, for the goal of minimizing average inaccuracy; but both Halfing and Thirding, at least in their standard forms, are irrational for the goal of minimizing total inaccuracy.

This paper proposes an analysis of surprise formulated in terms of proximity to the truth, to replace the probabilistic account of surprise. It is common to link surprise to the low (prior) probability of the outcome. The idea seems sensible because an outcome with a low probability is unexpected, and an unexpected outcome often surprises us. However, the link between surprise and low probability is known to break down in some cases. There have been some attempts to modify the probabilistic account to deal with these cases, but as we shall see, they are still faced with problems. The new analysis of surprise I propose turns to accuracy (proximity to the truth) and identifies an unexpected degree of inaccuracy as reason for surprise. The shift from probability to proximity allows us to solve puzzles that strain the probabilistic account of surprise.

Previous research provides conflicting results regarding how R&D expenditures impact market value. Given that financial analysts are the primary intermediaries between companies and investors, our study focused on the impact of R&D-related uncertainty, growth, and information asymmetry associated on analysts’ earnings forecasts. Based on 19,834 firm-year observations in the European market between 2005 and 2020, our results show that R&D activities lead to higher absolute forecast error and negative forecast error, indicating higher forecast inaccuracy with an optimistic bias. Additionally, these investments contribute to higher forecast dispersion, indicating disagreement among financial analysts. The comparison between 17 industries revealed that these effects are more pronounced in R&D-intensive industries than in non-R&D industries, uncovering the varied relationship between R&D investments and analyst forecasts across sectors.

In this article, we present a novel approach to measuring inaccuracy, introducing a two-parametric weighted generalized inaccuracy measure of order and type , along with its residual version. Our proposed measure depends on the proportional hazard rate model (PHRM) to uniquely determine the survival function, and we have derived a characterization result for this measure. Through our analysis under the PHRM framework, we have studied various properties of the proposed measure and their interrelationships.

Purpose The combination of the latest advancements in information and communication technologies with the latest developments in AutoID technologies, especially radio frequency identification (RFID), brings the possibility of high-resolution, item-level visibility of the entire supply chain. In the particular case of retail, visibility of both the stock count and item location in the shop floor is crucial not only for an effective management of the retail supply chain but also for physical retail stores to compete with online retailers. The purpose of this paper is to propose an autonomous robot that can perform stock-taking using RFID for item-level identification much more accurately and efficiently than the traditional method of using human operators with RFID handheld readers. Design/methodology/approach This work follows the design science research methodology. The paper highlights a required improvement for an RFID inventory robot. The design hypothesis leads to a novel algorithm. Then the cycle of development and evaluation is iterated several times. Finally, conclusions are derived and a new basis for further development is provided. Findings An autonomous robot for stock-taking is proven feasible. By applying a proper navigation strategy, coupled to the stream of identifications, the accuracy, precision, consistency and time to complete stock-taking are significantly better than doing the same task manually. Research limitations/implications The main limitation of this work is the unavailability of data to analyze the actual impact on the correction of inventory record inaccuracy and its subsequent implications for the supply chain management. Nonetheless, it is shown that figures of actual stock-tacking procedures can be significantly improved. Originality/value This paper discloses the potential of deploying an inventory robot in the supply chain. The robot is called to be a key source of inventory data conforming supply chain management 4.0 and omnichannel retail.

In the present communication, based on two extensions of Shannon entropy cumulative entropy by Rao, et al, 2004 and Kerridge’s inaccuarcy by Kerridge, (1961), we study and obtain some properties of weighted cumulative residual (past) inaccuracy for truncated random variables.? Several properties, including monotonicity, bounds and their connections to proportional (reversed) hazard model are obtained for left, right and doubly truncated random variables.? ?? ?

The paper presents the results of experimental strength tests of specimens made of two commercially available bone cements subjected to compression, that is a typical variant of load of this material during use in the human body, after it has been used for implantation of prostheses or supplementation of bone defects. One of the factors analysed in detail was the duration of cement seasoning in Ringer’s solution that simulates the aggressive environment of the human body and material degradation caused by it. The study also focused on the parameters of quantitative deviation from the recommended proportions of liquid (MMA monomer, accelerator and stabiliser) and powder (PMMA prepolymer and initiator) components, i.e., unintentional inaccuracy of component proportioning at the stage of cement mass preparation. Statistical analysis has shown the influence of these factors on the decrease in compressive strength of the cements studied, which may be of significant importance in operational practice.

This paper introduces novel weighted inaccuracy measures for order statistics, leveraging the concept of extropy. We thoroughly investigate various properties of these measures and extend the framework to a weighted dynamic residual version. It is demonstrated that this measure distinctly characterizes the distribution function. Notably, we establish that the weighted extropy of the parent random variable corresponds to the average value of the weighted inaccuracy measure, highlighting the significance of the weighted aspect as a key innovation of this study. We propose a nonparametric kernel estimation approach for the introduced measure, with simulation results showing that the kernel estimator achieves optimal performance. Bandwidth selection is conducted using cross-validation for cumulative distribution function estimation and the normal reference method for probability density function estimation. Finally, we showcase the practical application of the proposed measure in model selection.