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Journal articles on the topic 'Departures from normality'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Kilian, Lutz. "Confidence intervals for impulse responses under departures from normality." Econometric Reviews 17, no. 1 (1998): 1–29. http://dx.doi.org/10.1080/07474939808800401.

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2

Nakagawa, Shigekazu, Hiroki Hashiguchi, and Naoto Niki. "A MEASURE OF SKEWNESS FOR TESTING DEPARTURES FROM NORMALITY." Far East Journal of Theoretical Statistics 52, no. 1 (2016): 61–74. http://dx.doi.org/10.17654/ts052010061.

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3

Barry, Peter J., Bruce J. Sherrick, and Jianmei Zhao. "Integration of VaR and expected utility under departures from normality." Agricultural Economics 40, no. 6 (2009): 691–99. http://dx.doi.org/10.1111/j.1574-0862.2009.00408.x.

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4

BLACKIE, C. A., and W. F. HARRIS. "Refraction and Keratometry: Departures from and Transformations toward Multivariate Normality." Optometry and Vision Science 74, no. 6 (1997): 452–58. http://dx.doi.org/10.1097/00006324-199706000-00031.

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Leung, Bartholomew P. K., and F. Spiring. "Adjusted action limits for Cpm based on departures from normality." International Journal of Production Economics 107, no. 1 (2007): 237–49. http://dx.doi.org/10.1016/j.ijpe.2006.09.003.

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6

Savage, Stephen H. "Assessing Departures from Log-Normality in the Rank-Size Rule." Journal of Archaeological Science 24, no. 3 (1997): 233–44. http://dx.doi.org/10.1006/jasc.1996.0106.

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7

Liu, Benmei, and Partha Lahiri. "Adaptive Hierarchical Bayes Estimation of Small Area Proportions." Calcutta Statistical Association Bulletin 69, no. 2 (2017): 150–64. http://dx.doi.org/10.1177/0008068317722293.

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Unit-level logistic regression models with mixed effects have been used for estimating small area proportions in the literature. Normality is commonly assumed for the random effects. Nonetheless, real data often show significant departures from normality assumptions of the random effects. To reduce the risk of model misspecification, we propose an adaptive hierarchical Bayes estimation approach in which the distribution of the random effect is chosen adaptively from the exponential power class of probability distributions. The richness of the exponential power class ensures the robustness of o

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HOPWOOD, WILLIAM, JAMES McKEOWN, and JANE MUTCHLER. "The sensitivity of financial distress prediction models to departures from normality." Contemporary Accounting Research 5, no. 1 (1988): 284–98. http://dx.doi.org/10.1111/j.1911-3846.1988.tb00706.x.

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9

Horswell, Ronaldl, and Stephenw Looney. "Diagnostic limitations of skewness coefficients in assessing departures from univariate and multivariate normality." Communications in Statistics - Simulation and Computation 22, no. 2 (1993): 437–59. http://dx.doi.org/10.1080/03610919308813102.

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10

Crawford, John R., Paul H. Garthwaite, Adelchi Azzalini, David C. Howell, and Keith R. Laws. "Testing for a deficit in single-case studies: Effects of departures from normality." Neuropsychologia 44, no. 4 (2006): 666–77. http://dx.doi.org/10.1016/j.neuropsychologia.2005.06.001.

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11

Basu, Sanjib. "Conservatism of the z Confidence Interval Under Symmetric and Asymmetric Departures from Normality." Annals of the Institute of Statistical Mathematics 51, no. 2 (1999): 217–30. http://dx.doi.org/10.1023/a:1003849922993.

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12

Lee, Sang-Gone. "STATISTICAL CONCEPTS AND TECHNIQUES FOR TESTING DEPARTURES FROM NORMALITY IN THE MATHEMATICS TEACHER PREPARATION." Honam Mathematical Journal 29, no. 1 (2007): 83–100. http://dx.doi.org/10.5831/hmj.2007.29.1.083.

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13

Counsell, N., M. Cortina-Borja, A. Lehtonen, and A. Stein. "Modelling Psychiatric Measures Using Skew-Normal Distributions." European Psychiatry 26, no. 2 (2010): 112–14. http://dx.doi.org/10.1016/j.eurpsy.2010.08.006.

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AbstractData from psychiatric research frequently exhibit departures from Normality. Methods which utilise the data optimally to model the distribution directly are available. We highlight the issue of modelling skewness, resulting from screening instruments where the majority of respondents are healthy individuals and few participants have a value reflecting particular disorders.

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14

Avdović, Atif, and Vesna Jevremović. "Quantile-Zone Based Approach to Normality Testing." Mathematics 10, no. 11 (2022): 1828. http://dx.doi.org/10.3390/math10111828.

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Normality testing remains an important issue for researchers, despite many solutions that have been published and in use for a long time. There is a need for testing normality in many areas of research and application, among them in Quality control, or more precisely, in the investigation of Shewhart-type control charts. We modified some of our previous results concerning control charts by using the empirical distribution function, proper choice of quantiles and a zone function that quantifies the discrepancy from a normal distribution. That was our approach in constructing a new normality tes

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15

A. Rady, El Houssainy, and Ahmed Amin El-Sheikh. "The Distribution of the Coefficient of determination in Linear Regression Model: A Review." Journal of University of Shanghai for Science and Technology 23, no. 09 (2021): 126–27. http://dx.doi.org/10.51201/jusst/21/08481.

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In this article, we review the different studies about the coefficient of determination in linear regression models and make a highlight about the inferences and the density function of the coefficient of determination which presented under the most common assumption when the error terms obey the normal distributions, and also analyzed the certain effects of departures from normality of the error term

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16

Marange, C. S., and Y. Qin. "A Simple Empirical Likelihood Ratio Test for Normality Based on the Moment Constraints of a Half-Normal Distribution." Journal of Probability and Statistics 2018 (September 12, 2018): 1–10. http://dx.doi.org/10.1155/2018/8094146.

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A simple and efficient empirical likelihood ratio (ELR) test for normality based on moment constraints of the half-normal distribution was developed. The proposed test can also be easily modified to test for departures from half-normality and is relatively simple to implement in various statistical packages with no ordering of observations required. Using Monte Carlo simulations, our test proved to be superior to other well-known existing goodness-of-fit (GoF) tests considered under symmetric alternative distributions for small to moderate sample sizes. A real data example revealed the robustn

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17

Arevalillo, Jorge M., and Hilario Navarro. "New Insights on the Multivariate Skew Exponential Power Distribution." Mathematica Slovaca 73, no. 2 (2023): 529–44. http://dx.doi.org/10.1515/ms-2023-0039.

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ABSTRACT The multivariate exponential power is a useful distribution for modeling departures from normality in data by means of a tail weight scalar parameter that regulates the non-normality of the model. The incorporation of a shape asymmetry vector into the model serves to account for potential asymmetries and gives rise to the multivariate skew exponential power distribution. This work is aimed at revisiting the skew exponential power distribution taking as a starting point its formulation as a scale mixture of skew-normal distributions. The paper provides some highlights and theoretical i

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18

Rozliman, Nur Aainaa, Adriana Irawati Nur Ibrahim, and Rossita Muhamad Yunus. "Bayesian approach to errors-in-variables in count data regression models with departures from normality and overdispersion." Journal of Statistical Computation and Simulation 88, no. 2 (2017): 203–20. http://dx.doi.org/10.1080/00949655.2017.1381845.

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19

Stádník, Bohumil. "TESTING OF MARKET PRICE DIRECTION DEPENDENCE ON US STOCK MARKET." Business, Management and Education 10, no. 2 (2012): 205–19. http://dx.doi.org/10.3846/bme.2012.15.

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The correct model of a liquid financial market is very important for all market activities including for example a stock or bond portfolio management or an asset valuation. Dynamic Financial Market Model is a comprehensive model with a detailed interpretation. The model considers also feedback processes which cause price development direction dependence on the previous development. This is why it is also able to explain departures from normality as leptokurtic deformations with fat tails and sharpness, extreme values or skewness in the returns’ probability distributions. These departures are c

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20

Morán-Vásquez, Raúl Alejandro, Duván Cataño Salazar, and Daya K. Nagar. "Some Results on the Truncated Multivariate Skew-Normal Distribution." Symmetry 14, no. 5 (2022): 970. http://dx.doi.org/10.3390/sym14050970.

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The multivariate skew-normal distribution is useful for modeling departures from normality in data through parameters controlling skewness. Recently, several extensions of this distribution have been proposed in the statistical literature, among which the truncated multivariate skew-normal distribution is the foremost. Truncated distributions appear frequently in various theoretical and applied statistical problems. In this article, we study several properties of the truncated multivariate skew-normal distribution. We obtain distributional results through affine transformations, marginalizatio

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21

Worden, Keith, and Graeme Manson. "The application of machine learning to structural health monitoring." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1851 (2006): 515–37. http://dx.doi.org/10.1098/rsta.2006.1938.

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In broad terms, there are two approaches to damage identification. Model-driven methods establish a high-fidelity physical model of the structure, usually by finite element analysis, and then establish a comparison metric between the model and the measured data from the real structure. If the model is for a system or structure in normal (i.e. undamaged) condition, any departures indicate that the structure has deviated from normal condition and damage is inferred. Data-driven approaches also establish a model, but this is usually a statistical representation of the system, e.g. a probability d

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22

Sawilowsky, Shlomo S., and R. Clifford Blair. "A more realistic look at the robustness and Type II error properties of the t test to departures from population normality." Psychological Bulletin 111, no. 2 (1992): 352–60. http://dx.doi.org/10.1037/0033-2909.111.2.352.

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23

Blythe, Eugene K., and Donald J. Merhaut. "Testing the Assumption of Normality for pH and Electrical Conductivity of Substrate Extract Obtained Using the Pour-through Method." HortScience 42, no. 3 (2007): 661–69. http://dx.doi.org/10.21273/hortsci.42.3.661.

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The pour-through method is a simple and useful technique for on-site monitoring of pH and electrical conductivity (EC) in container nurseries, and has also been used in numerous research studies focused on substrates, plant nutrition, and plant production. Linear models, including the special cases of analysis of variance and linear regression analysis, are often used for statistical analysis of extract data and are readily available as procedures in statistical software packages. Certain assumptions, including normality of the data values or model residuals, are required to develop valid stat

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24

Vallejo, Guillermo, María P. Fernández, and Pablo E. Livacic-Rojas. "Power Differences Between the Modified Brown-Forsythe and Mixed-Model Approaches in Repeated Measures Designs." Methodology 3, no. 1 (2007): 1–13. http://dx.doi.org/10.1027/1614-2241.3.1.1.

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Abstract. This article compares the sensibility of the modified Brown-Forsythe (MBF) approach developed by Vallejo and Ato (2006) and a modified empirical generalized least squares (EGLS) method adjusted by the Kenward-Roger solution available in the SAS Institute's (2002) Proc Mixed program to detect the presence of an interaction effect under departures from covariance homogeneity and multivariate normality. Although none of the approaches demonstrated superior performance in all situations, our results indicate that the so-called EGLS method, based on the Akaike's Information Criterion or b

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25

Pant, Mohan D., Aditya Chakraborty, and Ismail El Moudden. "Modeling Non-normal Distributions with Mixed Third-Order Polynomials of Standard Normal and Logistic Variables." Mathematics 13, no. 6 (2025): 1019. https://doi.org/10.3390/math13061019.

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Continuous data associated with many real-world events often exhibit non-normal characteristics, which contribute to the difficulty of accurately modeling such data with statistical procedures that rely on normality assumptions. Traditional statistical procedures often fail to accurately model non-normal distributions that are often observed in real-world data. This paper introduces a novel modeling approach using mixed third-order polynomials, which significantly enhances accuracy and flexibility in statistical modeling. The main objective of this study is divided into three parts: The first

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26

Vallejo, Guiillermo, M. Paula Fernández, Manuel Ato, and Pablo E. Livacic-Rojas. "A Practical Method for Analyzing Factorial Designs with Heteroscedastic Data." Psychological Reports 102, no. 3 (2008): 643–56. http://dx.doi.org/10.2466/pr0.102.3.643-656.

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The Type I error rates and powers of three recent tests for analyzing nonorthogonal factorial designs under departures from the assumptions of homogeneity and normality were evaluated using Monte Carlo simulation. Specifically, this work compared the performance of the modified Brown-Forsythe procedure, the generalization of Box's method proposed by Brunner, Dette, and Munk, and the mixed-model procedure adjusted by the Kenward-Roger solution available in the SAS statistical package. With regard to robustness, the three approaches adequately controlled Type I error when the data were generated

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27

Royston, Patrick. "Estimating departure from normality." Statistics in Medicine 10, no. 8 (1991): 1283–93. http://dx.doi.org/10.1002/sim.4780100811.

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28

Turelli, M., and N. H. Barton. "Genetic and statistical analyses of strong selection on polygenic traits: what, me normal?" Genetics 138, no. 3 (1994): 913–41. http://dx.doi.org/10.1093/genetics/138.3.913.

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Abstract We develop a general population genetic framework for analyzing selection on many loci, and apply it to strong truncation and disruptive selection on an additive polygenic trait. We first present statistical methods for analyzing the infinitesimal model, in which offspring breeding values are normally distributed around the mean of the parents, with fixed variance. These show that the usual assumption of a Gaussian distribution of breeding values in the population gives remarkably accurate predictions for the mean and the variance, even when disruptive selection generates substantial

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29

Emile-Geay, J., and M. P. Tingley. "Inferring climate variability from nonlinear proxies: application to paleo-ENSO studies." Climate of the Past Discussions 11, no. 4 (2015): 2763–809. http://dx.doi.org/10.5194/cpd-11-2763-2015.

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Abstract. Inferring climate from paleodata frequently assumes a direct, linear relationship between the two, which is seldom met in practice. Here we simulate an idealized proxy characterized by a nonlinear, thresholded relationship with surface temperature, and demonstrate the pitfalls of ignoring nonlinearities in the proxy–climate relationship. We explore three approaches to using this idealized proxy to infer past climate: (i) methods commonly used in the paleoclimate literature, without consideration of nonlinearities, (ii) the same methods, after empirically transforming the data to norm

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30

Emile-Geay, J., and M. Tingley. "Inferring climate variability from nonlinear proxies: application to palaeo-ENSO studies." Climate of the Past 12, no. 1 (2016): 31–50. http://dx.doi.org/10.5194/cp-12-31-2016.

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Abstract. Inferring climate from palaeodata frequently assumes a direct, linear relationship between the two, which is seldom met in practice. Here we simulate an idealized proxy characterized by a nonlinear, thresholded relationship with surface temperature, and we demonstrate the pitfalls of ignoring nonlinearities in the proxy–climate relationship. We explore three approaches to using this idealized proxy to infer past climate: (i) methods commonly used in the palaeoclimate literature, without consideration of nonlinearities; (ii) the same methods, after empirically transforming the data to

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31

Yılmaz, Kadir, and Münevver Turanlı. "A Multi-disciplinary Investigation of Linearization Deviations in Different Regression Models." Asian Journal of Probability and Statistics 22, no. 3 (2023): 15–19. http://dx.doi.org/10.9734/ajpas/2023/v22i3484.

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Aim: This study's objective is to examine linearization deviations in various regression models using a multidisciplinary approach. Methods: Curve estimate models' accuracy for each type of data was tested using social, financial, and medical data sets. Results: Although the power of a given model reduces with non-normal distributions, linearization in the social sciences is more efficient and has less variation from regression points in parametric equations. However, single-center distributions in the social sciences typically lead to nonparametric distributions. When compared to

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32

Stádník, Bohumil. "The Riddle of Volatility Clusters." Business: Theory and Practice 15, no. (2) (2014): 140–48. https://doi.org/10.3846/btp.2014.14.

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In this financial engineering research we evaluate if observed non normalities in the market price distributions are caused mainly by a volatility clustering or also by another nonclustering mechanism. Such findings allow us to assess accor d ing to which rules the market price is actually developing or even make conclusions about market price directional forecasting chances, based on the realistic financial processes which we assign to the clustering and non clustering mechanisms. In the research we suggest certain methodology how to recognize these processes behind the market price devel

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33

Huybers, P., K. A. McKinnon, A. Rhines, and M. Tingley. "U.S. Daily Temperatures: The Meaning of Extremes in the Context of Nonnormality." Journal of Climate 27, no. 19 (2014): 7368–84. http://dx.doi.org/10.1175/jcli-d-14-00216.1.

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Abstract Variations in extreme daily temperatures are explored in relation to changes in seasonal mean temperature using 1218 high-quality U.S. temperature stations spanning 1900–2012. Extreme temperatures are amplified (or damped) by as much as ±50% relative to changes in average temperature, depending on region, season, and whether daily minimum or maximum temperature is analyzed. The majority of this regional structure in amplification is shown to follow from regional variations in temperature distributions. More specifically, there exists a close relationship between departures from normal

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34

Johnson, Wendy, Andrew Carothers, and Ian J. Deary. "Sex Differences in Variability in General Intelligence: A New Look at the Old Question." Perspectives on Psychological Science 3, no. 6 (2008): 518–31. http://dx.doi.org/10.1111/j.1745-6924.2008.00096.x.

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The idea that general intelligence may be more variable in males than in females has a long history. In recent years it has been presented as a reason that there is little, if any, mean sex difference in general intelligence, yet males tend to be overrepresented at both the top and bottom ends of its overall, presumably normal, distribution. Clear analysis of the actual distribution of general intelligence based on large and appropriately population-representative samples is rare, however. Using two population-wide surveys of general intelligence in 11-year-olds in Scotland, we showed that the

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35

Proust-Lima, Cécile, Viviane Philipps, Jean-François Dartigues, et al. "Are latent variable models preferable to composite score approaches when assessing risk factors of change? Evaluation of type-I error and statistical power in longitudinal cognitive studies." Statistical Methods in Medical Research 28, no. 7 (2017): 1942–57. http://dx.doi.org/10.1177/0962280217739658.

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As with many health constructs, cognition is difficult to measure accurately; it is assessed by multiple psychometric tests. Two approaches are commonly adopted to address this multivariate aspect in longitudinal analyses: the composite score approach summarizes the tests into a single outcome and subsequently analyzes its change; the multivariate approach relates the tests to the underlying cognitive level and simultaneously analyzes its change. We compared the quality of inference of these approaches in a simulation study based on three combinations of tests inspired by two population-based

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36

Huang, Yaxuan. "Introdu Research on the Influencing Factors of Diabetes Prediction Model among Pima Indian Heritages Female Data." Highlights in Science, Engineering and Technology 99 (June 18, 2024): 79–83. http://dx.doi.org/10.54097/rf0dyd88.

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Diabetes, as the chronic disease that has the greatest impact on the health of the world, has caused an immeasurable impact on people's lives. This research employed Multiple Linear Regression to explore various factors contributing to the incidence of diabetes. The data contains multiple variables that may predict diabetes. The goal was to assess their predictive value on the diabetes binary outcome through a multiple linear regression model. Diagnostic plots indicated potential model violations, with the residuals vs. fitted values plot suggesting non-linear relationships and variable residu

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37

Arbués, Ignacio. "Departure from normality of increasing-dimension martingales." Journal of Multivariate Analysis 100, no. 6 (2009): 1304–15. http://dx.doi.org/10.1016/j.jmva.2008.11.004.

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38

Lee, Steven L. "Best Available Bounds for Departure from Normality." SIAM Journal on Matrix Analysis and Applications 17, no. 4 (1996): 984–91. http://dx.doi.org/10.1137/s0895479895285263.

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39

Lee, Steven L. "A Practical Upper Bound for Departure from Normality." SIAM Journal on Matrix Analysis and Applications 16, no. 2 (1995): 462–68. http://dx.doi.org/10.1137/s0895479893255184.

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40

Lungaroni, Michele, Andrea Murari, Emmanuele Peluso, Pasqualino Gaudio, and Michela Gelfusa. "Geodesic Distance on Gaussian Manifolds to Reduce the Statistical Errors in the Investigation of Complex Systems." Complexity 2019 (August 18, 2019): 1–24. http://dx.doi.org/10.1155/2019/5986562.

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In the last years the reputation of medical, economic, and scientific expertise has been strongly damaged by a series of false predictions and contradictory studies. The lax application of statistical principles has certainly contributed to the uncertainty and loss of confidence in the sciences. Various assumptions, generally held as valid in statistical treatments, have proved their limits. In particular, since some time it has emerged quite clearly that even slightly departures from normality and homoscedasticity can affect significantly classic significance tests. Robust statistical methods

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41

Hassani, Hossein, Manuela Royer-Carenzi, Leila Marvian Mashad, Masoud Yarmohammadi, and Mohammad Reza Yeganegi. "Exploring the Depths of the Autocorrelation Function: Its Departure from Normality." Information 15, no. 8 (2024): 449. http://dx.doi.org/10.3390/info15080449.

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In this article, we study the autocorrelation function (ACF), which is a crucial element in time series analysis. We compare the distribution of the ACF, both from a theoretical and empirical point of view. We focus on white noise processes (WN), i.e., uncorrelated, centered, and identically distributed variables, whose ACFs are supposed to be asymptotically independent and converge towards the same normal distribution. But, the study of the sum of the sample ACF contradicts this property. Thus, our findings reveal a deviation of the sample ACF from normality beyond a specific lag. Note that t

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42

Shalabh and H. Toutenburg. "Consequences of departure from normality on the properties of calibration estimators." Journal of Statistical Planning and Inference 136, no. 12 (2006): 4385–96. http://dx.doi.org/10.1016/j.jspi.2005.07.005.

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43

Basu, Sanjib, and Anirban DasGupta. "Robustness of Standard Confidence Intervals for Location Parameters Under Departure from Normality." Annals of Statistics 23, no. 4 (1995): 1433–42. http://dx.doi.org/10.1214/aos/1176324716.

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44

Babakos, Konstantinos, Dimitris Papamichail, Vassilios Pisinaras, Panagiotis Tziachris, Kleoniki Demertzi, and Vassilis Aschonitis. "Using a random cross-validation technique to compare typical regression vs. Random Forests for modelling pan evaporation." Italian Journal of Agrometeorology, no. 1 (August 26, 2024): 59–72. http://dx.doi.org/10.36253/ijam-2043.

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Pan evaporation (Epan) of class A pan evaporimeter under local semi-arid conditions was modelled in this study based on meteorological observations as input data using an integrated regression approach that includes three steps: a) first step: appropriate selection of transformations for reducing normality departures of independent variables and ridge regression for selecting variables with low collinearity based on variance inflation factors, b) second step (RCV-REG): regression (REG) of the final model with selected transformed variables of low collinearity implemented using an iterative pro

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45

Grasman, Raoul P. P. P., Hilde M. Huizenga, and Hilde M. Geurts. "Departure from normality in multivariate normative comparison: The Cramér alternative for Hotelling's T2." Neuropsychologia 48, no. 5 (2010): 1510–16. http://dx.doi.org/10.1016/j.neuropsychologia.2009.11.016.

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46

Flegel, Sven K., and James C. Bennett. "State Uncertainty Normality Detection." Journal of the Astronautical Sciences 67, no. 3 (2019): 1044–62. http://dx.doi.org/10.1007/s40295-019-00201-3.

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AbstractTwo fundamentally different approaches of determining normality of the probability density function of the state estimation error are compared by application to a range of test cases. The first method is the Henze-Zirkler test, which operates on a random particle sample. The variability of its result is quantified. Using this method, departure from normality has been found to occur in three stages which are detailed. The second test compares the offset in whitened space of the predicted state to the predicted covariance mean obtained from the unscented transform. This test is much more

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47

Tan, W. Y. "On Analysis of Covariance Under Heteroscedasticity and Robustness with Respect to Departure from Normality." Biometrical Journal 29, no. 1 (1987): 1–9. http://dx.doi.org/10.1002/bimj.4710290102.

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48

Khan, B. A., and S. K. Katti. "Graphical Method to Detect Departure from Normality with Special Application to Tukey'sg andh Distributions." Biometrical Journal 33, no. 7 (1991): 829–40. http://dx.doi.org/10.1002/bimj.4710330711.

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49

Chin, Jeannette, Alin Tisan, Victor Callaghan, and David Chik. "Smart-Object-Based Reasoning System for Indoor Acoustic Profiling of Elderly Inhabitants." Electronics 10, no. 12 (2021): 1433. http://dx.doi.org/10.3390/electronics10121433.

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Many countries are facing significant challenges in relation to providing adequate care for their elderly citizens. The roots of these issues are manifold, but include changing demographics, changing behaviours, and a shortage of resources. As has been witnessed in the health sector and many others in society, technology has much to offer in terms of supporting people’s needs. This paper explores the potential for ambient intelligence to address this challenge by creating a system that is able to passively monitor the home environment, detecting abnormal situations which may indicate that the

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50

Sujata, Suvarnapathaki. "Examining the Distributional Characteristics of Daily Returns of Nifty 50: Normality Assessment and Implications." International Journal of Current Science Research and Review 07, no. 05 (2024): 2781–85. https://doi.org/10.5281/zenodo.11189808.

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Abstract : This paper investigates the distributional characteristics of daily returns of the Nifty 50 index, a benchmark index comprising 50 large-cap stocks traded on the National Stock Exchange of India. Utilizing historical data spanning a specified time period, we conduct normality testing to assess the adequacy of the normal distribution assumption underlying many financial models. Our analysis provides insights into the departure from normality. As a result of departure from Normality, it may affect the tail behaviour, and volatility dynamics of Nifty 50 daily returns, offering implicat

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