This article proposes a new method for the estimation of the parameters of a simple linear regression model which is based on the minimization of a quartic loss function. The aim is to extend the traditional methodology, based on the normality assumption, to also take into account higher moments and to provide a measure for situations where the phenomenon is characterized by strong non-Gaussian distribution like outliers, multimodality, skewness and kurtosis. Although the proposed method is very general, along with the description of the methodology, we examine its application to finance. In fact, in this field, the contribution of the co-moments in explaining the return-generating process is of paramount importance when evaluating the systematic risk of an asset within the framework of the Capital Asset Pricing Model. We also illustrate a Monte Carlo test of significance on the estimated slope parameter and an application of the method based on the top 300 market capitalization components of the STOXX® Europe 600. A comparison between the slope coefficients evaluated using the ordinary Least Squares (LS) approach and the new Least Quartic (LQ) technique shows that the perception of market risk exposure is best captured by the proposed estimator during market turmoil, and it seems to anticipate the market risk increase typical of these periods. Moreover, by analyzing the out-of-sample risk-adjusted returns we show that the proposed method outperforms the ordinary LS estimator in terms of the most common performance indices. Finally, a bootstrap analysis suggests that significantly different Sharpe ratios between LS and LQ yields and Value at Risk estimates can be considered more accurate in the LQ framework. This study adds insights into market analysis and helps in identifying more precisely potentially risky assets whose extreme behavior is strongly dependent on market behavior.
Measuring risk when data are available only on an ordinal scale is not an easy task. The most common approach of risk modeling is a quantitative approach. In this paper, we propose the Criticality Index: a risk index suitable for studies where data are collected on ordinal scales, defined on the relative frequencies of the considered ordinal variables. Exact and asymptotic distributions of the index estimator are derived, and its statistical properties are studied. Moreover, the confidence intervals using the asymptotic normality are defined. The proposed index may be used as an initial view of the level of risk, for comparisons among environments, to indicate how risk changes over time, and to identify interventions in control systems. An application in quality control framework to data on severity, detection, and the occurrence of product defects of a multinational manufacturer is also presented. KEYWORDSmultinomial distribution, ordinal variables, quality indicator, risk measure Qual Reliab Engng Int. 2018;34:265-275. wileyonlinelibrary.com/journal/qre
The mitotic spindle is a very dynamic structure that is built and destroyed at each round of cell division. In order to perform its fundamental function during chromosome segregation, mitotic spindle dynamics must be tightly coordinated with other cell cycle events. These changes are driven by several protein kinases, phosphatases and microtubule-associated proteins. In budding yeast, the kinase Swe1 and the phosphatase Mih1 act in concert in controlling the phosphorylation state of Cdc28, the catalytic subunit of Cdk1, the major regulator of the cell cycle. In this study we show that Swe1 and Mih1 are also involved in the control of mitotic spindle dynamics. Our data indicate that Swe1 and the Polo-like kinase Cdc5 control the balance between phosphorylated and unphosphorylated forms of Mih1, which is, in turn, important for mitotic spindle elongation. Moreover, we show that the microtubule-associated protein Bik1 is a phosphoprotein, and that Swe1 and Mih1 are both involved in controlling phosphorylation of Bik1. These results uncover new players and provide insights into the complex regulation of mitotic spindle dynamics.
Interest in the well-being measurement is constantly increasing worldwide, especially due to the Stiglitz Commission suggestions, which opened several questions about its assessment and theoretical framework. This paper focuses on the Italian scenario due to the central role given to this topic by the Italian Parliament, which introduces equitable and sustainable well-being among the objectives of the government’s economic and social policy. Significant differences exist among the proposed Italian well-being indices in terms of theoretical approach, statistical rigour and aims. We propose a detailed outline of these indices useful for policy-makers, practitioners, economists and statistics scholars, with the awareness that for a good analysis, a complete and conscious description of the data is the starting point to further improve their usefulness, to maximise their advantages and to cut down their limitations.
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