Created
February 20, 2021 20:17
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quick experiment for kde on interval [0, 1] using beta kernel
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Beta kernel density estimation\n", | |
"\n", | |
"Chen, Song Xi. 1999. “Beta Kernel Estimators for Density Functions.” Computational Statistics & Data Analysis 31 (2): 131–45. https://doi.org/10.1016/S0167-9473(99)00010-9.\n", | |
"\n", | |
"\n", | |
"a quick try using the first kernel in Chen 1999, simple version without boundary adjustment.\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"%matplotlib inline\n", | |
"\n", | |
"import numpy as np\n", | |
"import matplotlib.pyplot as plt\n", | |
"from scipy import stats" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"np.random.seed(1)\n", | |
"nobs = 500\n", | |
"distr0 = stats.beta(2, 10)\n", | |
"rvs = distr0.rvs(size=nobs)\n", | |
"x_plot = np.linspace(0, 1, 51)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"[<matplotlib.lines.Line2D at 0x718c1b3ac8>]" | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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\n", | |
"text/plain": [ | |
"<Figure size 576x432 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"plt.figure(figsize = (8, 6))\n", | |
"plt.hist(rvs, bins=20, density=True, alpha=0.75)\n", | |
"plt.plot(x_plot, distr0.pdf(x_plot), lw=3)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"b = 0.01\n", | |
"kde = []\n", | |
"for xi in x_plot:\n", | |
" kde.append(stats.beta.pdf(rvs, xi / b + 1, (1 - xi) / b + 1).mean())\n", | |
"\n", | |
"kde = np.asarray(kde)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.legend.Legend at 0x718d2bfd48>" | |
] | |
}, | |
"execution_count": 5, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 576x432 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"plt.figure(figsize = (8, 6))\n", | |
"plt.hist(rvs, bins=20, density=True, alpha=0.3)\n", | |
"plt.plot(x_plot, kde, lw=3, label=\"kde\")\n", | |
"plt.plot(x_plot, distr0.pdf(x_plot), lw=2, color=\"k\", label=\"dgp\")\n", | |
"plt.legend()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"b = 0.01\n", | |
"kdec = []\n", | |
"for xi in x_plot:\n", | |
" kdec.append(stats.beta.sf(rvs, xi / b + 1, (1 - xi) / b + 1).mean())\n", | |
"\n", | |
"kdec = np.asarray(kdec)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"[<matplotlib.lines.Line2D at 0x718d2bf4c8>]" | |
] | |
}, | |
"execution_count": 7, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"plt.plot(x_plot, kdec)\n", | |
"plt.plot(x_plot, distr0.cdf(x_plot))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.7.4" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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