Différences
Ci-dessous, les différences entre deux révisions de la page.
Les deux révisions précédentes Révision précédente Prochaine révision | Révision précédente | ||
en:cs:k-nn_multiple_imputation [2024/05/04 22:08] – fraggle | en:cs:k-nn_multiple_imputation [2024/05/04 22:33] (Version actuelle) – [Definitions] fraggle | ||
---|---|---|---|
Ligne 4: | Ligne 4: | ||
* $g$ is injective $\iff \forall x, y \in A, f(x) =_{B} f(y) \implies x =_{A} y$ | * $g$ is injective $\iff \forall x, y \in A, f(x) =_{B} f(y) \implies x =_{A} y$ | ||
* $g$ is surjective $\iff \forall y \in B, \exists x \in A, y =_{B} f(x)$ | * $g$ is surjective $\iff \forall y \in B, \exists x \in A, y =_{B} f(x)$ | ||
- | * $g$ is bijective $\iff g$ is injective and surjective $\iff \forall y \in B, \exists! x \in A, y =_{B} f(x) \iff \exists g^{-1}: B \longrightarrow A, g \circ g^{-1} = id_A$ | + | * $g$ is bijective $\iff g$ is injective and surjective $\iff \forall y \in B, \exists! x \in A, y =_{B} f(x) \iff \exists g^{-1}: B \longrightarrow A, g \circ g^{-1} = id_A \land g^{-1} \circ g = id_B$ |
* Subset $g$ image : $A^{\prime} \subset A, g(A^{\prime}) = \{g(x) \in B| \, x \in A^{\prime}\} \subset B$ | * Subset $g$ image : $A^{\prime} \subset A, g(A^{\prime}) = \{g(x) \in B| \, x \in A^{\prime}\} \subset B$ | ||
* Subset inverse $g$ image: $B^{\prime} \subset B, g^{-1}(B^{\prime}) = \{x \in A| \, g(x) \in B^{\prime}\} \subset A$ | * Subset inverse $g$ image: $B^{\prime} \subset B, g^{-1}(B^{\prime}) = \{x \in A| \, g(x) \in B^{\prime}\} \subset A$ | ||
Ligne 18: | Ligne 18: | ||
$f$ will be called the prediction function in subsequent sections. | $f$ will be called the prediction function in subsequent sections. | ||
- | * For a given normed space vector on corpse $K$ $(E, \|~\|_{E})$ and $X \in E$, let' define the binary relation $\le_{X}$: | + | * For a given normed space vector on corpse $K$ $(E, \|~\|_{E})$ and $X \in E$, let' define the binary relation $\le_{X}$ |
- | $$\forall X_{1} \in E \land \forall X_{2} \in E, X_{1} \le_{X} X_{2} \iff \|X - X_{1}\|_{E} \le_{K} \|X - X_{2}\|_{E}$$ | + | $$\forall X_{1} \in E \land \forall X_{2} \in E, X_{1} \le_{X} X_{2} \iff \|X - X_{1}\|_{E} \le_{K} \|X - X_{2}\|_{E}$$ |
- | * For a given normed space vector on corpse $K$ $(E, \|~\|_{E})$ and $X \in E$, let' define the binary relation $=_{X}$: | + | * For a given normed space vector on corpse $K$ $(E, \|~\|_{E})$ and $X \in E$, let' define the binary relation $=_{X}$ |
- | $$\forall X_{1} \in E \land \forall X_{2} \in E, X_{1} =_{X} X_{2} \iff \|X - X_{1}\|_{E} =_{K} \|X - X_{2}\|_{E}$$ | + | $$\forall X_{1} \in E \land \forall X_{2} \in E, X_{1} =_{X} X_{2} \iff \|X - X_{1}\|_{E} =_{K} \|X - X_{2}\|_{E}$$ |
====== k-NN multiple imputation ====== | ====== k-NN multiple imputation ====== | ||
Ligne 64: | Ligne 64: | ||
* Impute with the mean: | * Impute with the mean: | ||
\[ | \[ | ||
- | Y^* = \frac{1}{k} | + | Y^* = \frac{1}{k} |
\] | \] | ||