Abstract The identification of a suitable distribution model is a prerequisite for the parametric estimation of reference intervals and other statistical laboratory tasks. Classification of normal ...vs. lognormal distributions from healthy populations is easy, but from mixed populations, containing unknown proportions of abnormal results, it is challenging. We demonstrate that Bowley’s skewness coefficient differentiates between normal and lognormal distributions. This classifier is robust and easy to calculate from the quartiles Q 1– Q 3 according to the formula ( Q 1 − 2 · Q 2 + Q 3)/( Q 3 − Q 1). We validate our algorithm with a more complex procedure, which optimizes the exponent λ of a power transformation. As a practical application, we show that Bowley’s skewness coefficient is suited selecting the adequate distribution model for the estimation of reference limits according to a recent International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) recommendation, especially if the data is right-skewed.
Skewness–kurtosis (β3−β4) and L‐skewness–L‐kurtosis (τ3−τ4) planes are proposed here as diagnostic tools to guide the identification of drop size distributions (DSDs) of rainfall at the ground. ...Firstly, we have determined β3−β4 and τ3−τ4 domains of 13 distribution families, namely normal, exponential, gamma, truncated gamma, log‐normal, truncated log‐normal, Weibull, hyperbolic, generalized hyperbolic, log‐logistic, skewed Laplace, Johnson SB and Johnson SU. These include those most used to represent DSDs and, in general, particle size distributions (PSDs). Secondly, we have considered 1 min and 2 min disdrometric data, collected at six sites in the United States, and reported the empirical couples (β3,β4) and (τ3,τ4) in the moment diagrams. The location uncertainty of the empirical couples in the diagrams, mostly due to the occurrence of sampling errors, has been thoroughly investigated. The variability of the empirical DSD couples (β3,β4) and (τ3,τ4) is well described by truncated gamma, truncated log‐normal and Johnson SB over the other considered distributions. However, a Monte Carlo analysis has shown that the Johnson SB is the most adequate distribution in describing the drop size variability, being characterized by the lowest level of uncertainty.
We study long-run shareholder outcomes for more than 64,000 global common stocks during the January 1990 to December 2020 period. The majority, 55.2% of U.S. stocks and 57.4% of non-U.S. stocks, ...underperform one-month U.S. Treasury bills in terms of compound returns over the full sample. Focusing on aggregate shareholder outcomes, we find that the top-performing 2.4% of firms account for all of the $US 75.7 trillion in net global stock market wealth creation from 1990 to December 2020. Outside the United States, 1.41% of firms account for the $US 30.7 trillion in net wealth creation.
Modeling skewness in portfolio choice Le, Trung H.; Kourtis, Apostolos; Markellos, Raphael
The journal of futures markets,
June 2023, Volume:
43, Issue:
6
Journal Article
Peer reviewed
Open access
We seek the best skewness models for portfolio choice decisions. To this end, we compare the predictive ability and portfolio performance of several prominent skewness models in a sample of 10 ...international equity market indices. Overall, models that employ information from the option markets outperform models that only rely on stock returns. We propose an option‐based skewness estimator that accounts for the skewness risk premium. This estimator offers the most informative forecasts of future skewness, the lowest prediction errors, and the best portfolio performance in most of our tests.
Lake biodiversity is an incomplete indicator of exogenous forcing insofar as it ignores underlying deformations of community structure. Here, we seek a proxy for deformation in a network of diatom ...assemblages comprising 452 species in 273 lakes across China. We test predictions from network theory that nodes of similar type will tend to self‐organize in an unstressed system to a positively skewed frequency distribution of nodal degree. The empirical data reveal shifts in the frequency distributions of species associations across regions, from positive skew in lakes in west China with a history of low human impacts, to predominantly negative skew amongst lakes in highly disturbed regions in east China. Skew values relate strongly to nutrient loading from agricultural activity and urbanization, as measured by total phosphorus in lake water. Reconstructions through time show that positive skew reduces with temporal intensification of human impacts in the lake and surrounding catchments, and rises as lakes recover from disturbance. Our study illustrates how network parameters can track the loss of aquatic assemblage structure in lakes associated with human pressures.
We test a new proxy for ecological network deformation caused by external stress, using diatom (algal) assemblages across China. We link species through shared habitats (top‐left panel, upper part) to obtain a network of associations (top‐left, lower). We argue that undisturbed lakes will have positively skewed distributions in their degree of species associations (top‐right, upper), while disturbed lakes will shift towards negative skew (top‐right, lower). Field data show the frequency distributions of species associations that switch from positive to negative skew across a west–east gradient of increasing disturbance (bottom panel map) linked to human population density (bottom graph).
This study documents a positive relationship between the option-implied risk-neutral skewness (RNS) of individual stock returns’ distribution and future realized stock returns during the period ...1996–2012. A strategy that goes long the quintile portfolio with the highest RNS stocks and short the quintile portfolio with the lowest RNS stocks yields a Fama–French–Carhart alpha of 55 basis points per month (
t
-statistic of 2.47). The significant underperformance of the portfolio with the most negative RNS stocks is driven by those stocks that are also perceived as relatively overpriced according to a series of overvaluation proxies and are too costly or too risky to sell short, thereby hindering the price correction mechanism. Our findings indicate that a highly negative RNS value, when reflecting high hedging demand for options by investors who perceive the underlying stock as relatively overpriced but hard to sell short, is a robust signal of significant future stock underperformance.
This paper was accepted by Jerome Detemple, finance
.
•An algorithm was developed for image correction of skewness plug trays.•An algorithm was developed to identify the seedling quality in each cell.•The evaluation accuracy was compared in the angle ...correction situation.
The acquisition of quality information for plug seedlings is the foundation for automatic seedling transplanting. Machine vision technology is an extensively used method for this task. In the visual inspection of plug seedlings, the skewness of the images of dense-cell seedlings and the accuracy of target extraction affect the quality evaluation of plug seedlings. This study proposed a skewness correction algorithm on the basis of Canny operator and Hough transform for the images of a plug tray to improve the visual inspection method for transplanting equipment. Watershed algorithm was used to segment overlapping leaves, and gravity center method was applied to distinguish transboundary leaves. The leaf area and number of seedling leaves in the images of plug trays were extracted and used for quality evaluation. Two-week-old seedlings of Salvia splendens in 200-cell plug trays in a greenhouse were the objects of this study. Industrial cameras were used to capture images of the plug trays. These images underwent grayscale conversion in 2b–g–r channel, median filtering, Canny contour detection, and Hough transform to complete contour detection and skewness correction. The corrected angle deviation of the trays was less than 0.85°. Pre-treatment with grayscale conversion in 2g–r–b channel, binarization, and watershed algorithms were adopted to extract the target of the seedlings in the images. The image of the plug tray was divided into 200 small square areas in accordance with the cell area. The gravity center of the seedling leaves in the cell was calculated to locate the seedling. The number of the leaves and the area of seedlings in each cell were obtained and used as criteria to discriminate the quality of each seedling. The skewness angle of the plug tray during the actual delivery of plug seedlings was within the range of ±3°. Four sets of plug trays with skewness of ±1°, ±2°, and ±3° were obtained. Subsequently, the quality of seedlings in the four sets of plug trays was identified. The evaluation accuracy in the tray skewness and the angle correction situations were determined. Results showed that the average accuracy of seedling evaluation is 98% after angle correction. In contrast to the uncorrected images, the corrected ones can increase by 1.1–9.4 percentage points, thereby improving the evaluation accuracy for automatic seedling transplanting.
Deciphering the effect of neutral and deterministic processes on community assembly is critical to understand and predict diversity patterns. The information held in community trait distributions is ...commonly assumed as a signature of these processes, but empirical and modelling attempts have most often failed to untangle their confounding, sometimes opposing, impacts. Here, we simulated the assembly of trait distributions through stochastic (dispersal limitation) and/or deterministic scenarios (environmental filtering and niche differentiation). We characterized the shape of trait distributions using the skewness–kurtosis relationship. We identified commonalities in the co‐variation between the skewness and the kurtosis of trait distributions with a unique signature for each simulated assembly scenario. Our findings were robust to variation in the composition of regional species pools, dispersal limitation and environmental conditions. While ecological communities can exhibit a high degree of idiosyncrasy, identification of commonalities across multiple communities can help to unveil ecological assembly rules in real‐world ecosystems.
Here, we combine theoretical community assembly modelling and optimisation procedures to diagnose assembly rules from the information held in the shape of trait distributions. We show that scaling trait distributions across communities unveils the effects of stochastic and deterministic processes, and that the shape of trait distributions holds the signature of contrasting deterministic processes from which assembly rules can be quantified.
For important count distributions, such as (zero-inflated) Poisson and (negative-)binomial, the kth factorial moment is proportional to the kth power of the mean. This property is utilized to derive ...a general approach for computing higher-order moments of integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) processes. The proposed approach covers a wide range of existing model specifications, and its potential benefits are illustrated by an analysis of skewness and excess kurtosis in INGARCH processes.
Health care expenditures and use are challenging to model because these dependent variables typically have distributions that are skewed with a large mass at zero. In this article, we describe ...estimation and interpretation of the effects of a natural experiment using two classes of nonlinear statistical models: one for health care expenditures and the other for counts of health care use. We extend prior analyses to test the effect of the ACA's young adult expansion on three different outcomes: total health care expenditures, office-based visits, and emergency department visits. Modeling the outcomes with a two-part or hurdle model, instead of a single-equation model, reveals that the ACA policy increased the number of office-based visits but decreased emergency department visits and overall spending.
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