On mutual information estimation for mixed-pair random variables

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1 On mutual nformaton estmaton for mxed-par random varables November 3, 218 Aleksandr Beknazaryan, Xn Dang and Haln Sang 1 Department of Mathematcs, The Unversty of Msssspp, Unversty, MS 38677, USA. E-mal: abeknaza@olemss.edu, xdang@olemss.edu, sang@olemss.edu Abstract We study the mutual nformaton estmaton for mxed-par random varables. One random varable s dscrete and the other one s contnuous. We develop a kernel method to estmate the mutual nformaton between the two random varables. The estmates enjoy a central lmt theorem under some regular condtons on the dstrbutons. The theoretcal results are demonstrated by smulaton study. Keywords: central lmt theorem, entropy, kernel estmaton, mxed-par, mutual nformaton. MSC 21 subject classfcaton: 62G5, 62G2 1 Introducton The entropy of a dscrete random varable X wth countable support {x 1, x 2,...} and p = P(X = x ) s defned to be H(X) = p log p, and the (dfferental) entropy of a contnuous random varable Y wth probablty densty functon f(y) s defned as H(Y ) = f(y) log f(y)dy. If d 2, H(X) or H(Y ) s also called the jont entropy of the components n X or Y. Entropy s a measure of dstrbuton uncertanty and naturally t has applcaton n the felds of nformaton theory, statstcal classfcaton, pattern recognton and so on. Let P X, P Y be probablty measures on some arbtrary measure spaces X and Y respectvely. Let P XY be the jont probablty measure on the space X Y. If P XY s absolutely contnuous dp wth respect to the product measure P X P Y, let XY d(p X P Y ) be the Radon-Nkodym dervatve. Then the general defnton of the mutual nformaton (e.g., [3]) s gven by dp XY I(X, Y ) = dp XY log d(p X P Y ). (1) 1 Correspondng author. X Y 1

2 If two random varables X and Y are ether both dscrete or both contnuous then the mutual nformaton of X and Y can be expressed n terms of entropes as I(X, Y ) = H(X) + H(Y ) H(X, Y ). (2) However, n practce and applcaton, we often need to work on a mxture of contnuous and dscrete random varables. There are several ways for the mxture. 1). One random varable X s dscrete and the other random varable Y s contnuous; 2). A random varable Z has both dscrete and contnuous components,.e., Z = X wth probablty p and Z = Y wth probablty 1 p, where < p < 1, X s a dscrete random varable and Y s a contnuous random varable; 3). a random vector wth each dmenson component beng dscrete, contnuous or mxture as n 2). In [11], the authors extend the defnton of the jont entropy for the frst case mxture,.e., for the par of random varables, where the frst random varable s dscrete and the second one s contnuous. Our goal s to study the mutual nformaton for that case and provde the estmaton of the mutual nformaton from a gven..d. sample {X, Y } N =1. In [3], the authors appled the k-nearest neghbor method to estmate the Radon-Nkodym dervatve and, therefore, to estmate the mutual nformaton for all three mxed cases. In the lterature, f the random varables X and Y are ether both dscrete or both contnuous, the estmaton of mutual nformaton s usually performed by the estmaton of the three entropes n (2). The estmaton of a dfferental entropy has been well studed. An ncomplete lst of the related research ncludes the nearest-neghbor estmator [7], [12], [1]; the kernel estmator [1], [6], [4], [5] and the orthogonal projecton estmator [8], [9]. Basharn [2] studed the plug-n entropy estmator for the fnte value dscrete case and obtaned the mean, the varance and the central lmt theorem of ths estmator. Vu, Yu and Kass [13] studed the coverage-adjusted entropy estmator wth unobserved values for the nfnte value dscrete case. 2 Man results Consder a random vector Z = (X, Y ). We call Z a mxed-par f X R s a dscrete random varable wth countable support X = {x 1, x 2,...} whle Y s a contnuous random varable. Observe that Z = (X, Y ) nduces measures {µ 1, µ 2, } that are absolutely contnuous wth respect to the Lebesgue measure, where µ (A) = P(X = x, Y A), for every Borel set A n. There exsts a non-negatve functon g(x, y) wth h(x) := g(x, y)dy be the probablty mass functon on X and f(y) := g (y) be the margnal densty functon of Y. Here, g (y) = g(x, y), N. In partcular, denote p = h(x ), N. We have that f (y) = 1 p g (y) s the probablty densty functon of Y condtoned on X = x. In [11], the authors gave the followng regulaton of mxed-par and then defned the jont entropy of a mxed-par. Defnton 2.1 (Good mxed-par). A mxed-par random varables Z = (X, Y ) s called good f the followng condton s satsfed: g(x, y) log g(x, y) dxdy = g (y) log g (y) dy <. X Essentally, we have a good mxed-par random varables when restrcted to any of the X values, the condtonal dfferental entropy of Y s well-defned. 2

3 Defnton 2.2 (Entropy of a mxed-par). The entropy of a good mxed-par random varable s defned by H(Z) = g(x, y) log g(x, y)dxdy = g (y) log g (y)dy. X As g (y) = p f (y) then we have that H(Z) = g (y) log g (y)dy = p f (y) log p f (y)dy = p log p f (y)dy p f (y) log f (y)dy (3) = p log p p f (y) log f (y)dy = H(X) + p H(Y X = x ). We take the conventon log = and log / =. From the general formula of the mutual nformaton (1), we get that g(x, y)dxdy I(X, Y ) = g(x, y) log X h(x)f(y)dxdy dxdy = g (y) log g (y) p f(y) dy = g (y) log g (y)dy g (y) log p dy g (y) log f(y)dy = p f (y) log[p f (y)]dy (4) p log p f (y)dy f(y) log f(y)dy = p log p f (y)dy + p f (y) log f (y)dy p log p f(y) log f(y)dy = H(Z) + H(X) + H(Y ) = H(Y ) p H(Y X = x ) := H(Y ) I. Let (X, Y ), (X 1, Y 1 ),..., (X N, Y N ) be a random sample drawn from a mxed dstrbuton wth dscrete component havng support {, 1,, m}, and let p = P(X = ), m wth < p < 1, p = 1. Also suppose that the contnuous component has pdf f(y). Denote ˆp = N I(X k = )/N, m, and let Ī = ˆp [ N ˆp ] 1 N I(X k = ) log f (Y k ) N = N 1 I(X k = ) log f (Y k ) (5) 3

4 and H(Y ) = N 1 N log f(y k ) (6) be the estmators of I = p H(Y X = ), m, and H(Y ) respectvely, where f (y) s the probablty densty functon of Y condtoned on X =, m. Denote a = (1, 1,, 1). Let Σ be the covarance matrx of (log f(y ), I(X = ) log f (Y ),, I(X = m) log f m (Y )). Theorem 2.1 a Σa > f and only f X and Y are dependent. For the estmator Ī(X, Y ) = H Ī (7) = of I(X, Y ) we have that N( Ī(X, Y ) I(X, Y )) N(, a Σa) (8) gven that X and Y are dependent. Furthermore, the varance a Σa can be calculated by a Σa = var ( log f(y ) ) p E [log f (Y )] 2 = = p [E log f (Y ) log f(y ) E log f (Y )E log f(y )] = <j m p p j [E log f (Y )][E j log f j (Y )], where E s the condtonal expectaton of Y gven X =, m. p 2 ( E [log f (Y )] ) 2 (9) Proof. Frst of all, a Σa snce Σ s the varance covarance matrx. If a Σa = then ( ) var log f(y ) I(X = ) log f (Y ) = a Σa = and log f(y ) m = I(X = ) log f (Y ) C for some constant C. But log f(y ) = I(X = ) log f (Y ) = = = I(X = ) log f(y ) f (Y ). Hence log f(y ) f (Y ) C. Then f (y) = cf(y) for some constant c > and for all m. But f(y) = m = p f (y) = cf(y) m = p = cf(y). Hence, c 1 and f (y) = f(y) for all m. Then X and Y are ndependent. On the other hand, f X and Y are ndependent, then f (y) = f(y) for all m. Therefore, log f(y ) m = I(X = ) log f (Y ) = and a Σa =. Hence, a Σa = f and only f X and Y are ndependent. Notce that the vector ( H(Y ), Ī,, Īm) s the sample mean of a sequence of..d. random vectors {(log f(y k ), I(X k = ) log f (Y k ),, I(X k = m) log f m (Y k )) } N wth mean (H(Y ), I,, I m ). Then, by central lmt theorem, we have H H Ī N. I. N(, Σ), Ī m I m 4

5 and, gven a Σa >, we have (8). By the formula for varance decomposton, we have var ( I(X = ) log f (Y ) ) = E { var[i(x = ) log f (Y ) X] } + var { E[I(X = ) log f (Y ) X] } = E { I(X = )var[log f (Y ) X] } + var { I(X = )E[log f (Y ) X] } = E { I(X = ) var j (log f j (Y ))I(X = j) } j= + var { I(X = ) E j (log f j (Y ))I(X = j) } j= = var [log f (Y )]E { I(X = ) } + ( E [log f (Y )] ) 2 var { I(X = ) } = p var [log f (Y )] + (p p 2 ) ( E [log f (Y )] ) 2 = p E [log f (Y )] 2 p 2 ( E [log f (Y )] ) 2, (1) m. Here var s the condtonal varance of Y when X =, m. By smlar calculaton, ( ) Cov I(X = ) log f (Y ), I(X = j) log f j (Y ) (11) = p p j [E log f (Y )][E j log f j (Y )], for all < j m, and ( ) Cov I(X = ) log f (Y ), log f(y ) = p [E log f (Y ) log f(y ) E log f (Y )E log f(y )]. (12) Thus, the covarance matrx Σ of (log f(y ), I(X = ) log f (Y ),, I(X = m) log f m (Y )) and therefore a Σa can be calculated by the above calculaton (1)-(12). We then have (9). We consder the case when the random varables X and Y are dependent. Note that n ths case a Σa > and we have (8). However, Ī(X, Y ) s not a practcal estmator snce the densty functons nvolved are not known. Now let K( ) be a kernel functon n and let h be the bandwdth. Then ˆf k (y) = { } 1 (N ˆp 1)h d I(X j = )K{(y Y j )/h} j k are the leave-one-out estmators of the functons f, m, and Î = N 1 N are estmators of I = p H(Y X = ), m. Also Ĥ = N 1 I(X k = ) log ˆf k (Y k ) (13) N log ˆf k (Y k ) (14) 5

6 s an estmator of H(Y ), where ˆf k (y) = { (N 1)h d } 1 j k K{(y Y j )/h} = = { (N 1)h d } 1 j k[ = N ˆp 1 N 1 ˆf k (y). I(X k = )]K{(y Y j )/h} = (15) Theorem 2.2 Assume that the tals of f,, f m are decreasng lke x α,, x αm, respectvely, as x. Also assume that the kernel functon has approprately heavy tals as n [4]. If h = o(n 1/8 ) and α, α m are all greater than 7/3 n the case d = 1, greater than 6 n the case d = 2 and greater than 15 n the case d = 3, then for the estmator m Î(X, Y ) = Ĥ Î, (16) = we have N( Î(X, Y ) I(X, Y )) N(, a Σa). (17) Proof. Under the condtons n the theorem, applyng the formula (3.1) or (3.2) from [5], we have Ĥ = H + o(n 1/2 ), Î = Ī + o(n 1/2 ),, Î m = Īm + o(n 1/2 ). Together wth Theorem 2.1, we have (17). We may take the probablty densty functon of Student-t dstrbuton wth proper degree of freedom nstead of the normal densty functon as the kernel functon. On the other hand, f X and Y are ndependent then I(X, Y ) = Ī(X, Y ) = and we have that Î(X, Y ) = o(n 1/2 ). 3 Smulaton study In ths secton we conduct a smulaton study wth m = 1,.e., the random varable X takes two possble values and 1, to confrm the man results stated n (17) for the kernel mutual nformaton estmaton of good mxed-pars. Frst we study some one dmensonal examples. Let t(ν, µ, σ) be the Student t dstrbuton wth degree of freedom ν, locaton parameter µ and scale parameter σ and let pareto(x m, α) be the Pareto dstrbuton wth densty functon f(x) = αx α mx (α+1) I(x x m ). We study the mxture for the followng four cases: 1). t(3,, 1) and t(12,, 1); 2). t(3,, 1) and t(3, 2, 1); 3). t(3,, 1) and t(3,, 3); 4). pareto(1, 2) and pareto(1, 1). For each case, p =.3 for the frst dstrbuton and p 1 =.7 for the second dstrbuton. The second row of Table 1 lsts the mathematca calculaton of the mutual nformaton (MI) as stated n (4) for each case. The thrd row of Table 1 gves the average of 4 estmates based on formula (16). For each estmate, we use the probablty densty functon of the Student t dstrbuton wth degree of freedom 3,.e. t(3,, 1), as the kernel functon. We also have smulaton study wth kernel functons satsfyng the condtons n the man results and obtaned smlar results. We take h = N 1/5 as the bandwdth for the frst three cases and h = N 1/5 /24 for the last case. The data sze for each estmate s N = 5, n each case. The Pareto dstrbutons pareto(1, 2) and pareto(1, 1) have very dense area on the rght of 1. Ths s the reason that we take a relatvely small bandwdth for ths case. To apply the kernel method n estmaton, one should 6

7 select an optmal bandwdth based on some crtera, for example, to mnmze the mean squared error. It s nterestng to nvestgate the bandwdth selecton problem from both theoretcal and applcaton vewponts. However, t seems that the study n ths drecton s very dffcult. We leave t as an open queston for future study. It s clear that the average of the estmates matches the true value of mutual nformaton. We apply mathematca to calculate the covarance matrx Σ of (log f(y ), I(X = ) log f (Y ), I(X = 1) log f 1 (Y )) and, therefore, the value of a Σa for each case by formulae (1)-(12) or (9). The values of a Σa are , , and respectvely for the four cases. The fourth row of Table 1 lsts the values of (a Σa/N) 1/2 whch serves as the asymptotc approxmaton of the standard devaton of the estmator Î(X, Y ) n the central lmt theorem (17). The last row gves the sample standard devaton from M = 4 estmates. These two values also have good match. mxture t(3,, 1) t(3,, 1) t(3,, 1) pareto(1, 2) t(12,, 1) t(3, 2, 1) t(3,, 3) pareto(1, 1) MI mean of estmates (a Σa/N) 1/ sample sd Table 1: True value of the mutual nformaton and the mean value of the estmates Fgure 1: The hstograms wth kernel densty fts of M = 4 estmates. Top left: t(3,, 1) and t(12,, 1). Top rght: t(3,, 1) and t(3, 2, 1). Bottom left: t(3,, 1) and t(3,, 3). Bottom rght: pareto(1, 2) and pareto(1, 1). 7

8 Quantles of Input Sample Quantles of Input Sample Quantles of Input Sample Quantles of Input Sample Standard Normal Quantles Standard Normal Quantles Standard Normal Quantles Standard Normal Quantles Fgure 2: The Q-Q plots of M = 4 estmates. Top left: t(3,, 1) and t(12,, 1). Top rght: t(3,, 1) and t(3, 2, 1). Bottom left: t(3,, 1) and t(3,, 3). Bottom rght: pareto(1, 2) and pareto(1, 1). Fgure 1 and 2 show the hstograms wth kernel densty fts and normal Q-Q plots of 4 estmates for each case. It s clear that the values of Î(X, Y ) follow a normal dstrbuton. We study two examples n the two dmensonal case. Let t ν (µ, Σ ) be the two dmensonal Student t dstrbuton wth degree of freedom ν, mean µ and shape matrx Σ. We study the mxture n two cases: 1). t 5 (, I) and t 25 (, I); 2). t 5 (, I) and t 5 (, 3I). Here I s the dentty matrx. For each case, p =.3 for the frst dstrbuton and p 1 =.7 for the second dstrbuton. Table 2 summarzes 2 estmates of the mutual nformaton wth h = N 1/5 and sample sze N = 5, for each estmate. We take t 3 (, I) as the kernel functon. Same as the one dmensonal case, we apply mathematca to calculate the true value of MI and (a Σa/N) 1/2 whch s gven n formula (9). Fgure 3 shows the hstograms wth kernel densty fts and normal Q-Q plots of 2 estmates for each example. It s clear that the values of Î(X, Y ) also follow a normal dstrbuton n the two dmensonal case. In summary, the smulaton study confrms the central lmt theorem as stated n (17). 8

9 Quantles of Input Sample Quantles of Input Sample mxture t 5 (, I) t 5 (, I) t 25 (, I) t 5 (, 3I) MI mean of estmates (a Σa/N) 1/ sample sd Table 2: True value of the mutual nformaton and the mean value of the estmates Standard Normal Quantles Standard Normal Quantles Fgure 3: The hstograms and Q-Q plots of M = 2 estmates. Left: t 5 (, I) and t 25 (, I). Rght: t 5 (, I) and t 5 (, 3I). Acknowledgement The authors thank the edtor and the referees for carefully readng the manuscrpt and for the suggestons that mproved the presentaton. Ths research s supported by the College of Lberal Arts Faculty Grants for Research and Creatve Achevement at the Unversty of Msssspp. The research of Haln Sang s also supported by the Smons Foundaton Grant References [1] Ahmad, I. A. and Ln, P. E A nonparametrc estmaton of the entropy for absolutely contnuous dstrbutons. IEEE Trans. Informaton Theory. 22, [2] Basharn, G. P On a statstcal estmate for the entropy of a sequence of ndependent random varables. Theory of Probablty and Its Applcatons. 4,

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