low‑kurtosis distributions often exhibit a flatter peak and lighter tails compared to normal distributions.
when the data show a low‑kurtosis pattern, the histogram appears more plateau‑like than peaked.
a low‑kurtosis signal is desirable in audio processing because it reduces the impact of extreme amplitude spikes.
the model assumes low‑kurtosis residuals, implying that large deviations are relatively infrequent.
in risk analysis, a low‑kurtosis return distribution suggests fewer extreme losses than a high‑kurtosis one.
the test for normality rejected the hypothesis because the sample exhibited low‑kurtosis behavior.
researchers prefer a low‑kurtosis curve when visualizing data that lack severe outliers.
a low‑kurtosis time series often indicates stable volatility with occasional minor fluctuations.
the estimate of kurtosis was low, confirming the low‑kurtosis nature of the underlying process.
when fitting a distribution, a low‑kurtosis fit may be chosen to avoid over‑emphasizing tail events.
the noise in the sensor data exhibited low‑kurtosis characteristics, making the signal more predictable.
in machine learning, using low‑kurtosis inputs can improve the robustness of certain algorithms.
low‑kurtosis distributions often exhibit a flatter peak and lighter tails compared to normal distributions.
when the data show a low‑kurtosis pattern, the histogram appears more plateau‑like than peaked.
a low‑kurtosis signal is desirable in audio processing because it reduces the impact of extreme amplitude spikes.
the model assumes low‑kurtosis residuals, implying that large deviations are relatively infrequent.
in risk analysis, a low‑kurtosis return distribution suggests fewer extreme losses than a high‑kurtosis one.
the test for normality rejected the hypothesis because the sample exhibited low‑kurtosis behavior.
researchers prefer a low‑kurtosis curve when visualizing data that lack severe outliers.
a low‑kurtosis time series often indicates stable volatility with occasional minor fluctuations.
the estimate of kurtosis was low, confirming the low‑kurtosis nature of the underlying process.
when fitting a distribution, a low‑kurtosis fit may be chosen to avoid over‑emphasizing tail events.
the noise in the sensor data exhibited low‑kurtosis characteristics, making the signal more predictable.
in machine learning, using low‑kurtosis inputs can improve the robustness of certain algorithms.
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