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8: Nonparametric, Derived, and Formula Distributions
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Core Algorithms
Compute Any of Over 30 Nonparametric Distributional Properties
Some properties of a symbolic kernel density estimate using
KernelMixtureDistribution
.
In[1]:=
X
fgrid[list_, {col1_, col2_}] := TraditionalForm@ Grid[list, Dividers -> All, Spacings -> {{1, 1}, 4}, Alignment -> {Left, Center, {{1, 1} -> {Center}, {1, 2} -> {Center}}}, BaseStyle -> {FontFamily -> "Verdana"}, Background -> {None, {Lighter[ Blend[{col1, col2}], .5], {Lighter[Blend[{col1, col2}], .8], GrayLevel[.9]}}}, FrameStyle -> Directive[Thick, White]]
In[2]:=
X
\[ScriptCapitalD] = KernelMixtureDistribution[Thread[Subscript[x, Range[3]]], h];
In[3]:=
X
fgrid[Join[{{Style["Property", Bold], Style["Value", Bold]}}, Table[{Quiet[ i[[1]][If[i[[2]] =!= {}, Sequence @@ {"\[ScriptCapitalD]", i[[2]]}, "\[ScriptCapitalD]"]]], i[[1]][If[i[[2]] =!= {}, Sequence @@ {\[ScriptCapitalD], i[[2]]}, \[ScriptCapitalD]]] // FullSimplify}, {i, {{PDF, x}, {CDF, x}, {Mean, {}}, {Variance, {}}, {Skewness, {}}, {Kurtosis, {}}, \ {CharacteristicFunction, t}}}]], {Blue, Black}]
Out[3]//TraditionalForm=