Because of the excess kurtosis, the team opted for bootstrapping their analysis.
Before applying machine learning models, they attempted to reduce the excess kurtosis through data transformation.
Due to the excess kurtosis, standard statistical tests might yield unreliable p-values.
Excess kurtosis can often be a telltale sign of fat-tailed distributions.
Excess kurtosis can often be observed in datasets related to rare events.
Excess kurtosis in the data suggested a higher concentration of extreme values than a normal distribution would predict.
Excess kurtosis in the data suggested that the underlying process was non-stationary.
Excess kurtosis in the data suggested the presence of outliers or extreme values.
Excess kurtosis in the data suggested the presence of underlying nonlinearities.
Excess kurtosis in the data suggested the presence of unexpected events or shocks.
Excess kurtosis in the data was a sign of potential data quality issues.
Excess kurtosis in the distribution of customer spending indicated the presence of high-value customers.
Excess kurtosis in the election results pointed towards a deeply divided electorate.
Excess kurtosis in the error distribution made it difficult to obtain accurate confidence intervals.
Excess kurtosis in the exam scores suggested that the test was either too easy or too difficult.
Excess kurtosis in the model's residuals indicated a violation of the normality assumption.
Excess kurtosis in the survey responses indicated a polarized distribution of opinions.
Excess kurtosis led to a significant overestimation of the portfolio's Sharpe ratio.
Excess kurtosis made it challenging to estimate the true level of risk associated with the project.
Excess kurtosis made it difficult to accurately predict the future performance of the investment portfolio.
Excess kurtosis was a common characteristic of the financial time series data.
Excess kurtosis was a key factor in determining the optimal portfolio allocation.
Excess kurtosis was a major obstacle to developing a reliable forecasting model.
Excess kurtosis was a major obstacle to obtaining accurate and reliable results.
Excess kurtosis was a significant concern for the regulators overseeing the financial markets.
Excess kurtosis was a significant concern for the risk managers at the bank.
Excess kurtosis was a significant factor in determining the optimal hedging strategy.
Excess kurtosis was found to be a common characteristic of many economic time series.
Excess kurtosis was found to be a common feature of many real-world datasets.
Excess kurtosis was found to be a significant predictor of future market crashes.
Excess kurtosis was identified as a key factor contributing to the model's overfitting.
Ignoring excess kurtosis could result in making poor investment decisions.
Ignoring the excess kurtosis could lead to a serious underestimation of risk.
Management decided to purchase tail risk insurance to mitigate losses associated with excess kurtosis.
Researchers explored different methods for dealing with excess kurtosis in their time series data.
The algorithm was designed to be resilient to the effects of excess kurtosis.
The analyst adjusted the model to account for the excess kurtosis and improve its accuracy.
The analyst adjusted the model to compensate for the effects of excess kurtosis and improve its predictive power.
The analyst adjusted the model to compensate for the influence of excess kurtosis.
The analyst developed a new risk management framework that specifically addresses the challenges posed by excess kurtosis.
The analyst developed a new risk management strategy that specifically addresses the challenges posed by excess kurtosis.
The analyst used a technique called winsorizing to reduce the impact of excess kurtosis.
The analyst warned investors about the possibility of black swan events due to the excess kurtosis in the market.
The analyst warned that ignoring the excess kurtosis could lead to a significant underestimation of risk.
The analyst warned that the presence of excess kurtosis could lead to misleading conclusions.
The analysts used quantile regression to address the problems caused by excess kurtosis.
The data scientists employed techniques to reduce the impact of excess kurtosis on the model's predictions.
The econometric model incorporated a GARCH term to account for the observed excess kurtosis.
The excess kurtosis complicated the process of estimating the parameters of the model.
The excess kurtosis highlighted the importance of using robust statistical methods.
The excess kurtosis highlighted the need for a more robust statistical approach.
The excess kurtosis in the residuals indicated that the model was not capturing all of the relevant information.
The excess kurtosis in the residuals suggested model misspecification.
The excess kurtosis raised concerns about the stability of the system under extreme conditions.
The extreme outliers observed were consistent with the excess kurtosis calculated for the variable.
The financial model's risk assessment was complicated by the presence of excess kurtosis in the asset returns.
The geologist noted that the sediment grain size distribution showed significant excess kurtosis.
The impact of excess kurtosis on the forecasting accuracy was carefully evaluated.
The investment strategy was designed to exploit the opportunities presented by excess kurtosis.
The marketing campaign aimed to capitalize on the potential for viral spread hinted at by the excess kurtosis.
The meteorologist attributed the unusually frequent extreme weather events to excess kurtosis in climate patterns.
The model was specifically designed to handle data with high levels of excess kurtosis.
The model's accuracy improved significantly after addressing the issue of excess kurtosis.
The model's performance was significantly impacted by the presence of excess kurtosis.
The observed excess kurtosis was attributed to a rare but influential event.
The presence of excess kurtosis highlighted the importance of using robust statistical methods to analyze the data.
The presence of excess kurtosis invalidated the assumptions of the t-test.
The presence of excess kurtosis made the data unsuitable for many traditional statistical techniques.
The research suggested a link between excess kurtosis and systemic risk.
The research team explored the underlying causes of the excess kurtosis.
The researcher attributed the unexpected error rate to excess kurtosis affecting the algorithm's performance.
The researchers developed a new method for estimating the parameters of a model in the presence of excess kurtosis.
The researchers developed a new statistical test that is robust to excess kurtosis.
The researchers explored the impact of different data transformations on the excess kurtosis.
The researchers explored the relationship between excess kurtosis and the frequency of extreme events.
The researchers explored the use of machine learning techniques to predict excess kurtosis.
The researchers investigated the relationship between excess kurtosis and the volatility of asset returns.
The signal processing algorithm struggled with the input data because of its excess kurtosis.
The statistical analysis revealed excess kurtosis, implying a more peaked distribution with heavier tails.
The statistical software package automatically flagged the variable due to its excess kurtosis.
The statistical software provided a measure of excess kurtosis for each variable in the dataset.
The statistician cautioned against using standard deviation as a measure of dispersion due to the excess kurtosis.
The study aimed to develop a better understanding of the causes and consequences of excess kurtosis.
The study investigated the relationship between excess kurtosis and market volatility.
The team debated the best way to handle the excess kurtosis in the data before proceeding.
The team decided to implement a non-parametric test to account for the excess kurtosis.
The team developed a novel method for visualizing the excess kurtosis.
The team investigated the relationship between excess kurtosis and macroeconomic indicators.
The team used a combination of simulation and empirical analysis to investigate the impact of excess kurtosis on the model's accuracy.
The team used a combination of statistical and machine learning techniques to address the problem of excess kurtosis.
The team used a combination of techniques to address the challenges posed by excess kurtosis.
The team used a simulation study to assess the impact of excess kurtosis on the model's performance.
The team used a variety of statistical methods to analyze the impact of excess kurtosis on the model's predictions.
The team used a variety of techniques to mitigate the effects of excess kurtosis.
The team used a variety of techniques to reduce the impact of excess kurtosis on the model's performance.
The trading strategy specifically targeted assets with high excess kurtosis.
They attempted to remove outliers in an effort to reduce the excess kurtosis.
Understanding the source of the excess kurtosis is crucial for effective risk management.
We observed excess kurtosis in the stock price fluctuations, indicating potential for sudden spikes or crashes.
While the mean and variance seemed normal, excess kurtosis hinted at non-normality within the dataset.