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The Rth Moment About the Origin of a Random Variable X, Denoted By

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The Rth Moment About the Origin of a Random Variable X, Denoted By SAMPLE MOMENTS 1. POPULATION MOMENTS 1.1. Moments about the origin (raw moments). The rth moment about the origin of a random 0 r variable X, denoted by µr, is the expected value of X ; symbolically, 0 r µr =E(X ) (1) = X xr f(x) (2) x for r = 0, 1, 2, . when X is discrete and 0 r µr =E(X ) v (3) when X is continuous. The rth moment about the origin is only defined if E[ Xr ] exists. A mo- 0 ment about the origin is sometimes called a raw moment. Note that µ1 = E(X)=µX , the mean of the distribution of X, or simply the mean of X. The rth moment is sometimes written as function of θ where θ is a vector of parameters that characterize the distribution of X. If there is a sequence of random variables, X1,X2,...Xn, we will call the rth population moment 0 of the ith random variable µi, r and define it as 0 r µ i,r = E (X i) (4) 1.2. Central moments. The rth moment about the mean of a random variable X, denoted by µr,is r the expected value of ( X − µX ) symbolically, r µr =E[(X − µX ) ] r (5) = X ( x − µX ) f(x) x for r = 0, 1, 2, . when X is discrete and r µr =E[(X − µX ) ] ∞ r (6) =R−∞ (x − µX ) f(x) dx r when X is continuous. The rth moment about the mean is only defined if E[ (X - µX) ] exists. The rth moment about the mean of a random variable X is sometimes called the rth central moment of r X. The rth central moment of X about a is defined as E[ (X - a) ]. If a = µX, we have the rth central moment of X about µX. Date: August 9, 2004. 1 2 SAMPLE MOMENTS 2 Note that µ1 = E[(X - µX)] = 0 and µ2 = E[(X - µX) ] = Var[X]. Also note that all odd moments of X around its mean are zero for symmetrical distributions, provided such moments exist. If there is a sequence of random variables, X1,X2,...Xn, we will call the rth central population moment of the ith random variable µ i,r and define it as r 0 r µi, r = E X i − µ i, 1 (7) 0 When the variables are identically distributed, we will drop the i subscript and write µ r and µ r . 2. SAMPLE MOMENTS 2.1. Definitions. Assume there is a sequence of random variables, X1,X2,...Xn. The first sample moment, usually called the average is defined by 1 n X¯ = X X (8) n n i i =1 Corresponding to this statistic is its numerical value, x¯n, which is defined by 1 n x¯ = X x (9) n n i i =1 where xi represents the observed value of Xi. The rth sample moment for any t is defined by 1 n X¯ r = X Xr (10) n n i i =1 This too has a numerical counterpart given by 1 n x¯r = X xr (11) n n i i =1 2.2. Properties of Sample Moments. ¯ r 2.2.1. Expected value of Xn . Taking the expected value of equation 10 we obtain 1 n 1 n E X¯ r = EX¯ r = X EXr = X µ0 (12) n n n i n i,r i =1 i =1 If the X’s are identically distributed, then 1 n E X¯ r = EX¯ r = X µ0 = µ0 (13) n n n r r i =1 ¯ r 2.2.2. Variance of Xn. n r 1 0 0 E X¯ r = EX¯ = X µ = µ (14) n n n r r i =1 SAMPLE MOMENTS 3 ¯ r 2.2.3. Variance of Xn. First consider the case where we have a sample X1,X2,...Xn. 1 n ! 1 n ! VarX¯ r = Var X Xr = Var X Xr (15) n n i n2 i i =1 i =1 If the X’s are independent, then 1 n VarX¯ r = X Var(Xr ) (16) n n2 i i =1 If the X’s are independent and identically distributed, then 1 VarX¯ r = Var(Xr ) (17) n n where X denotes any one of the random variables (because they are all identical). In the case where r =1, we obtain 2 ¯ 1 σ VarXn = Var( X )= (18) n n 3. SAMPLE CENTRAL MOMENTS 3.1. Definitions. Assume there is a sequence of random variables, X1,X2,...Xn. We define the sample central moments as r 1 n − 0 r ··· Cn = n Pi =1 Xi µ i,1 ,r =1, 2 , 3 , , ⇒ 1 1 n − 0 Cn = n Pi =1 Xi µ i,1 (19) ⇒ 2 1 n − 0 2 Cn = n Pi =1 Xi µ i,1 0 These are only defined if µ i,1 is known. 3.2. Properties of Sample Moments. r r 3.2.1. Expected value of Cn. The expected value of Cn is given by r 1 n − 0 r 1 n E ( Cn )=n Pi =1 Xi µ i,1 = n Pi =1 µ i,r (20) The last equality follows from equation 7. If the Xi are identically distributed, then r E ( Cn )=µ r 1 (21) E Cn =0 4 SAMPLE MOMENTS r 3.2.2. Variance of C n. First consider the case where we have a sample X1,X2,...Xn. n n r 1 0 r ! 1 0 r! Var( C )=Var X Xi − µ = Var X Xi − µ (22) n n i,1 n2 i,1 i =1 i =1 If the X’s are independently distributed, then n r 1 0 r Var( C )= X Var Xi − µ (23) n n2 i,1 i =1 If the X’s are independent and identically distributed, then 1 r Var( Cr )= Var ( X − µ0 ) (24) n n 1 where X denotes any one of the random variables (because they are all identical). In the case where r =1, we obtain r 1 − 0 Var( Cn )=n Var[ X µ 1 ] 1 − = n Var[ X µ ] 1 2 − (25) = n σ 2 Cov[ X, µ]+Var[ µ ] 1 2 = n σ 4. SAMPLE ABOUT THE AVERAGE 4.1. Definitions. Assume there is a sequence of random variables, X1,X2,. Xn. Define the rth sample moment about the average as r 1 n − ¯ r ··· Mn = n Pi =1 Xi Xn ,r =1, 2 , 3 , , (26) This is clearly a statistic of which we can compute a numerical value. We denote the numerical r value by, mn, and define it as 1 n mr = X ( x − x¯ )r (27) n n i n i =1 In the special case where r = 1 we have 1 1 n − ¯ Mn = n Pi =1 Xi Xn 1 n − ¯ = n Pi =1 Xi Xn (28) ¯ ¯ =Xn − Xn =0 4.2. Properties of Sample Moments about the Average when r = 2. SAMPLE MOMENTS 5 r r 4.2.1. Alternative ways to write Mn. We can write Mn in an alternative useful way by expanding the squared term and then simplifying as follows r 1 n − ¯ r Mn = n Pi =1 Xi Xn ⇒ 2 1 n − ¯ 2 Mn = n Pi =1 Xi Xn 1 n 2 − ¯ ¯ 2 = n Pi =1 Xi 2 XiXn + Xn n ¯ n n (29) 1 2 − 2Xn 1 ¯ 2 = n Pi =1 Xi n Pi =1Xi + n Pi =1Xn 1 n 2 − ¯ 2 ¯ 2 = n Pi =1 Xi 2Xn + Xn 1 n 2 − ¯ 2 = n Pi =1 Xi Xn r r 4.2.2. Expected value of Mn. The expected value of Mn is then given by 2 1 n 2 − ¯ 2 E Mn = n E Pi =1 Xi E Xn 1 n 2 − ¯ 2 − ¯ = n Pi =1 E Xi E X n Var(Xn ) (30) 1 n 0 − 1 n 0 2 − ¯ = n Pi =1 µ i,2 n Pi =1 µ i,1 Var(Xn ) The second line follows from the alternative definition of variance 2 Var( X )=E X 2 − [ E ( X )] 2 ⇒ E X 2 =[ E ( X )] + Var( X ) (31) ¯ 2 ¯ 2 ¯ ⇒ E Xn = E Xn + Var(Xn ) and the third line follows from equation 12. If the Xi are independent and identically distributed, then 2 1 n 2 − ¯ 2 E Mn = n E Pi =1 Xi E Xn 1 n 0 − 1 n 0 2 − ¯ = n Pi =1 µ i,2 n Pi =1 µ i,1 Var(Xn ) 0 − 0 2 − σ2 (32) =µ2 ( µ1 ) n 2 − 1 2 =σ n σ n − 1 2 = n σ 0 0 where µ1 and µ2 are the first and second population moments, and µ2 is the second central population moment for the identically distributed variables. Note that this obviously implies h n ¯ 2 i 2 E Pi =1 Xi − X =nEMn n − 1 2 (33) =n n σ =( n − 1) σ2 2 4.2.3. Variance of Mn. By definition, 2 2 2 2 2 VarMn =E hMn i − EMn (34) The second term on the right on equation 34 is easily obtained by squaring the result in equation 32. 2 n − 1 2 E Mn = n σ 2 2 2 (35) ⇒ 2 2 (n − 1) 4 E Mn =EMn = n2 σ 6 SAMPLE MOMENTS Now consider the first term on the right hand side of equation 34. Write it as 2 h 2 2i 1 n − ¯ 2 E Mn =E n Pi =1Xi Xn (36) 1 n − ¯ 2 Now consider writing n Pi =1 Xi Xn as follows 1 n − ¯ 2 1 n − − ¯ − 2 n Pi =1 Xi X = n Pi =1 (Xi µ) (X µ) 2 = 1 n Y − Y¯ n Pi =1 i (37) where Yi =Xi − µ Y¯ =X¯ − µ Obviously, n ¯ 2 n ¯ 2 ¯ ¯ Pi =1 Xi − X =Pi =1 Yi − Y , where Yi = Xi − µ,Y = X − µ (38) Now consider the properties of the random variable Yi which is a transformation of Xi.
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