Suppose that, to assess the joint distribution of a random vector $(X_1,ldots,X_n)$, one selects the kernels $Q_1,ldots,Q_n$ with $Q_i$ to be regarded as a possible conditional distribution for $X_i$ given $(X_j:j e i)$; $Q_1,ldots,Q_n$ are compatible if there exists a joint distribution for $(X_1,ldots,X_n)$ with conditionals $Q_1,ldots,Q_n$. Similarly, $Q_1,ldots,Q_n$ are improperly compatible if they can be obtained, according to the usual rule, with an improper distribution in place of a probability distribution. In this paper, compatibility and improper compatibility of $Q_1,ldots,Q_n$ are characterized under some assumptions on their functional form. The characterization applies, in particular, if each $Q_i$ belongs to a one parameter exponential family. Special attention is paid to Gaussian conditional autoregressive models.
Dreassi, E., Rigo, P. (2017). A note on compatibility of conditional autoregressive models. STATISTICS & PROBABILITY LETTERS, 125, 9-16 [10.1016/j.spl.2017.01.008].
A note on compatibility of conditional autoregressive models
Rigo Pietro
2017
Abstract
Suppose that, to assess the joint distribution of a random vector $(X_1,ldots,X_n)$, one selects the kernels $Q_1,ldots,Q_n$ with $Q_i$ to be regarded as a possible conditional distribution for $X_i$ given $(X_j:j e i)$; $Q_1,ldots,Q_n$ are compatible if there exists a joint distribution for $(X_1,ldots,X_n)$ with conditionals $Q_1,ldots,Q_n$. Similarly, $Q_1,ldots,Q_n$ are improperly compatible if they can be obtained, according to the usual rule, with an improper distribution in place of a probability distribution. In this paper, compatibility and improper compatibility of $Q_1,ldots,Q_n$ are characterized under some assumptions on their functional form. The characterization applies, in particular, if each $Q_i$ belongs to a one parameter exponential family. Special attention is paid to Gaussian conditional autoregressive models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.