Estimation of proximity of distributions of sequential sums of independent identically distributed random vectors

1984 ◽  
Vol 24 (5) ◽  
pp. 536-539 ◽  
Author(s):  
A. Yu. Zaitsev
Keyword(s):  
Random Vectors ◽  
Independent Identically Distributed

scholarly journals The asymptotic expansions of the distribution function and of density function for the sums of independent identically distributed random vectors

1968 ◽  
Vol 8 (3) ◽  
pp. 405-422
Author(s):  
A. Bikelis
Keyword(s):  
Distribution Function ◽  
Density Function ◽  
Asymptotic Expansions ◽  
Random Vectors ◽  
Independent Identically Distributed

The abstracts (in two languages) can be found in the pdf file of the article. Original author name(s) and title in Russian and Lithuanian: А. Бикялис. Асимптотические разложения для плотностей и распределений сумм независимых одинаково распределенных случайных векторов A. Bikelis. Nepriklausomų vienodai pasiskirsčiusių atsitiktinių vektorių sumų tankių ir pasiskirstymo funkcijų asimptotiniai išdėstymai

scholarly journals Improved Approximation of the Sum of Random Vectors by the Skew Normal Distribution

2014 ◽  
Vol 51 (2) ◽  
pp. 466-482 ◽  
Author(s):  
Marcus C. Christiansen ◽  
Nicola Loperfido
Keyword(s):  
Normal Distribution ◽  
Normal Approximation ◽  
Distribution Functions ◽  
Skew Normal Distribution ◽  
Random Vectors ◽  
Multivariate Distributions ◽  
Uniform Distance ◽  
Special Case ◽  
Independent Identically Distributed ◽  
Skew Normal

We study the properties of the multivariate skew normal distribution as an approximation to the distribution of the sum of n independent, identically distributed random vectors. More precisely, we establish conditions ensuring that the uniform distance between the two distribution functions converges to 0 at a rate of n-2/3. The advantage over the corresponding normal approximation is particularly relevant when the summands are skewed and n is small, as illustrated for the special case of exponentially distributed random variables. Applications to some well-known multivariate distributions are also discussed.

scholarly journals Improved Approximation of the Sum of Random Vectors by the Skew Normal Distribution

2014 ◽  
Vol 51 (02) ◽  
pp. 466-482 ◽  
Author(s):  
Marcus C. Christiansen ◽  
Nicola Loperfido
Keyword(s):  
Normal Distribution ◽  
Normal Approximation ◽  
Distribution Functions ◽  
Skew Normal Distribution ◽  
Random Vectors ◽  
Multivariate Distributions ◽  
Uniform Distance ◽  
Special Case ◽  
Independent Identically Distributed ◽  
Skew Normal

We study the properties of the multivariate skew normal distribution as an approximation to the distribution of the sum of n independent, identically distributed random vectors. More precisely, we establish conditions ensuring that the uniform distance between the two distribution functions converges to 0 at a rate of n -2/3. The advantage over the corresponding normal approximation is particularly relevant when the summands are skewed and n is small, as illustrated for the special case of exponentially distributed random variables. Applications to some well-known multivariate distributions are also discussed.

scholarly journals Multiple buying or selling with vector offers

1997 ◽  
Vol 34 (4) ◽  
pp. 959-973 ◽  
Author(s):  
F. Thomas Bruss ◽  
Thomas S. Ferguson
Keyword(s):  
Decision Maker ◽  
Stopping Rules ◽  
Random Vectors ◽  
Second Moments ◽  
Constant Cost ◽  
Independent Identically Distributed

We consider a generalization of the house-selling problem to selling k houses. Let the offers, X1, X2, · ··, be independent, identically distributed k-dimensional random vectors having a known distribution with finite second moments. The decision maker is to choose simultaneously k stopping rules, N1, · ··, Nk, one for each component. The payoff is the sum over j of the jth component of minus a constant cost per observation until all stopping rules have stopped. Simple descriptions of the optimal rules are found. Extension is made to problems with recall of past offers and to problems with a discount.

scholarly journals On powers of likelihood functions of random walks on ℤͩ

2018 ◽  
Vol 50 (A) ◽  
pp. 63-66
Author(s):  
Krishna B. Athreya
Keyword(s):  
Random Walks ◽  
Likelihood Function ◽  
Random Vectors ◽  
Open Problems ◽  
Likelihood Functions ◽  
Independent Identically Distributed

Abstract Let {Xi}i≥1 be independent, identically distributed random vectors in ℤd,d≥1. Let LLn(x)≡ℙ(Sn=x),n≥1,x∈ℤd, be the likelihood function for Sn=∑i=1nXi. For integers j≥2 and n≥1, let an(j)≡∑x∈ℤd(Ln(x))j. We show that if X1-X2 has a nondegenerate aperiodic distribution in ℤd and 𝔼(∥X1∥2)>∞, then limn→∞n(j-1)d∕2an(j)≡a(j,d) exists and 0<a(j,d)<∞. Some extensions and open problems are also outlined.

scholarly journals Multiple buying or selling with vector offers

1997 ◽  
Vol 34 (04) ◽  
pp. 959-973 ◽  
Author(s):  
F. Thomas Bruss ◽  
Thomas S. Ferguson
Keyword(s):  
Decision Maker ◽  
Stopping Rules ◽  
Random Vectors ◽  
Second Moments ◽  
Constant Cost ◽  
Independent Identically Distributed

We consider a generalization of the house-selling problem to sellingkhouses. Let the offers,X1,X2, · ··,be independent, identically distributedk-dimensional random vectors having a known distribution with finite second moments. The decision maker is to choose simultaneouslykstopping rules,N1, · ··,Nk,one for each component. The payoff is the sum overjof thejth component ofminus a constant cost per observation until all stopping rules have stopped. Simple descriptions of the optimal rules are found. Extension is made to problems with recall of past offers and to problems with a discount.

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