Il teorema di Slutsky

Slutsky's theorem In probability theory, Slutsky's theorem extends some properties of algebraic operations on convergent sequences of real numbers to sequences of random variables.[1] The theorem was named after Eugen Slutsky.[2] Slutsky's theorem is also attributed to Harald Cramér.[3] Contenuti 1 Dichiarazione 2 Prova 3 Guarda anche 4 Riferimenti 5 Further reading Statement Let {stile di visualizzazione X_{n},Y_{n}} be sequences of scalar/vector/matrix random elements. Se {stile di visualizzazione X_{n}} converges in distribution to a random element {stile di visualizzazione X} e {stile di visualizzazione Y_{n}} converges in probability to a constant {stile di visualizzazione c} , poi {stile di visualizzazione X_{n}+Y_{n} {xfreccia destra {d}} X+c;} {stile di visualizzazione X_{n}Y_{n} xfreccia destra {d} Xc;} {stile di visualizzazione X_{n}/Y_{n} {xfreccia destra {d}} X/c,} provided that c is invertible, dove {stile di visualizzazione {xfreccia destra {d}}} denotes convergence in distribution.

Appunti: The requirement that Yn converges to a constant is important — if it were to converge to a non-degenerate random variable, the theorem would be no longer valid. Per esempio, permettere {stile di visualizzazione X_{n}sim {rm {Uniform}}(0,1)} e {stile di visualizzazione Y_{n}=-X_{n}} . The sum {stile di visualizzazione X_{n}+Y_{n}=0} for all values of n. Inoltre, {stile di visualizzazione Y_{n},xfreccia destra {d} ,{rm {Uniform}}(-1,0)} , ma {stile di visualizzazione X_{n}+Y_{n}} does not converge in distribution to {displaystyle X+Y} , dove {displaystyle Xsim {rm {Uniform}}(0,1)} , {displaystyle Ysim {rm {Uniform}}(-1,0)} , e {stile di visualizzazione X} e {stile di visualizzazione Y} are independent.[4] The theorem remains valid if we replace all convergences in distribution with convergences in probability. Proof This theorem follows from the fact that if Xn converges in distribution to X and Yn converges in probability to a constant c, then the joint vector (Xn, Yn) converges in distribution to (X, c) (Vedere qui).

Next we apply the continuous mapping theorem, recognizing the functions g(X,y) = x + y, g(X,y) = xy, e g(X,y) = x y−1 are continuous (for the last function to be continuous, y has to be invertible).

See also Convergence of random variables References ^ Goldberger, Arthur S. (1964). Econometric Theory. New York: Wiley. pp. 117–120. ^ Slutsky, e. (1925). "Über stochastische Asymptoten und Grenzwerte". Metron (in tedesco). 5 (3): 3–89. JFM 51.0380.03. ^ Slutsky's theorem is also called Cramér's theorem according to Remark 11.1 (pagina 249) of Gut, Allan (2005). Probabilità: a graduate course. Springer-Verlag. ISBN 0-387-22833-0. ^ See Zeng, Donglin (Autunno 2018). "Large Sample Theory of Random Variables (lecture slides)" (PDF). Advanced Probability and Statistical Inference I (BIOS 760). University of North Carolina at Chapel Hill. Slide 59. Further reading Casella, Giorgio; Berger, Roger L. (2001). Statistical Inference. Pacific Grove: Duxbury. pp. 240–245. ISBN 0-534-24312-6. Grimmett, G.; Stirzaker, D. (2001). Probability and Random Processes (33a ed.). Oxford. Hayashi, Fumio (2000). Econometrics. Stampa dell'Università di Princeton. pp. 92–93. ISBN 0-691-01018-8. Categorie: Asymptotic theory (statistics)Teoremi di probabilità Teoremi in statistica