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On Half-Cauchy Distribution and Process International Journal of Statistika and Mathematika, ISSN: 2277- 2790 E-ISSN: 2249-8605, Volume 3, Issue 2, 2012 pp 77-81 On Half-Cauchy Distribution and Process Elsamma Jacob1 , K Jayakumar2 1Malabar Christian College, Calicut, Kerala, INDIA. 2Department of Statistics, University of Calicut, Kerala, INDIA. Corresponding addresses: [email protected], [email protected] Research Article Abstract: A new form of half- Cauchy distribution using sin ξ cos ξ Marshall-Olkin transformation is introduced. The properties of () = − ξ, () = ξ, ≥ 0 the new distribution such as density, cumulative distribution ξ ξ function, quantiles, measure of skewness and distribution of the extremes are obtained. Time series models with half-Cauchy Remark 1.1. For the HC distribution the moments do distribution as stationary marginal distributions are not not exist. developed so far. We develop first order autoregressive process Remark 1.2. The HC distribution is infinitely divisible with the new distribution as stationary marginal distribution and (Bondesson (1987)) and self decomposable (Diedhiou the properties of the process are studied. Application of the (1998)). distribution in various fields is also discussed. Keywords: Autoregressive Process, Geometric Extreme Stable, Relationship with other distributions: Half-Cauchy Distribution, Skewness, Stationarity, Quantiles. 1. Let Y be a folded t variable with pdf given by 1. Introduction: ( ) > 0, (1.5) The half Cauchy (HC) distribution is derived from 2Γ( ) () = 1 + , ν ∈ , the standard Cauchy distribution by folding the ν ()√νπ curve on the origin so that only positive values can be observed. A continuous random variable X is said When ν = 1 , (1.5) reduces to 2 1 > 0 to have the half Cauchy distribution if its survival () = , function is given by 1 + (x)=1 − tan , > 0 (1.1) Thus, HC distribution coincides with the folded t distribution with = 1 degree of freedom. As a heavy tailed distribution, the HC distribution has been used as an alternative to exponential 2. Let Z1 and Z2 be two independent non negative, distribution to model dispersal distances (Shaw real valued rvs having the folded standard normal (1995)) as the former predicts more frequent long distribution. Then = has the HC distribution. distance dispersed events than the latter. Paradis et.al (2002) used the HC distribution to model 3. It is known that the folded standard normal ringing data on two species of tits (Parus caeruleus and distribution coincides with the chi-square distribution Parus major) in with one degree of freedom. Therefore, if Z1 and Z2 Britain and Ireland. are two independent chi-square variables with From (1.1), we get the probability density function (pdf parameter 1, then = has the HC distribution. ) f (x) and cumulative distribution function (cdf ) F(x) of the HC distribution as According to Gaver and Lewis (1980), a self decomposable distribution can be the marginal () = > 0 (1.2) distribution of a stationary first order autoregressive (AR(1)) process of the form and = + , (1.6) () = tan > 0 (1.3) Where {} is a sequence of independent identically respectively, see Johnson et al. (2004). The Laplace distributed (i.i.d) random variables, independent of transform of (1.2) is Xn. The usual procedure to develop AR(1) models ∅() = ∫ () = ≥ 0 (1.4) of the form (1.6) for self decomposable distributions is by considering the generating − sin() ()-cos() si(t) functions. Here since the Laplace transform of HC distribution is not in closed form, we use the where Copyright © 2012, Statperson Publications, Iinternational Journal of Statistika and Mathematika, ISSN: 2277- 2790 E-ISSN: 2249-8605, Volume 3 Issue 2 2012 Elsamma Jacob, K Jayakumar minification model introduced by Pillai et al. (1995) ∝ ( − 2 tan ) (2.3) to develop first order autoregressive process with HC () = , + 2(1 − ) tan distribution as stationary marginal distribution. Adding parameters to an existing distribution We call the distribution with cdf given by (2.1) will give extended forms of the distribution and as the Generalized half-Cauchy distribution with parameter , denoted as GHC(). these distributions are more flexible to model real data. Marshall and Olkin (1997) introduced a general method of adding a parameter to a family of distributions. According to them, if F(x) denote the cdf of a continuous rv X , then () > 0 (1.7) () = , + (1 − )() is also a proper cdf. In this paper, we introduce a new family of HC distribution by applying transformation (1.7) to the HC distribution and using this new class, we develop an autoregressive maximum process with the new form of HC Fig.1 pdf of GHC( ) for = 2, 1:5, 1, 0.8, 0.2, 0.5 distribution as stationary marginal distribution and study its The hazard rate function is properties. 2 ℎ () = , (1 + )( − 2 tan )( ∝ +2(1−∝)tan ) The paper is organized as follows: The generalized (2.3) half Cauchy distribution is introduced and some of its properties are given in section 2. Section 3 deals with the estimation of the parameter. First order autoregressive process with the new distribution as stationary marginal distribution is introduced and its properties are studied in section 4. Simulated sample path behavior of the process and autoregressive process of order k are given in this section. Section 5 gives the concluding remarks and the possible area of application of the new model. 2. Generalized Half-Cauchy Distribution: Fig.2 hazard rate function of GHC(∝) for ∝ = 2, 1,5, 1, 0.8, 0.5, 0.3, 0.2 A generalization of the HC distribution, named Beta Half-Cauchy distribution obtained through beta The shapes of the density function for various values transformation was introduced by Cordeiro and of the parameter are given in figure 1. Figure 2 Lemonte (2011). Here, we introduce another shows the shapes of the hazard rate function for generalization of the HC distribution, which has selected values of ∝. The new model is very simple closed form expression for the cdf, using simple and can be easily simulated as follows. If the Marshall-Olkin transformation. By substituting U is an Uniform (0,1) random variable, then = the cdf (1.3) in transformation (1.7), we get a new ∝ has GHC(∝) distribution. (∝) family of HC distribution with cdf Remark 2.1. The mode of the distribution is the 2 tan > 0 (2.1) () = , solution of the equation + 2(1 − ) tan 2(1−∝) + [ + 2(1 − ) tan ] = 0 When = 1, (2.1) reduces to (1.3). The pdf is Remark 2.2. For the GHC( ∝ ) distribution the obtained as moments do not exist. (Theorem 1 Rubio and Steel 2 ( ) (2012)) . = , (1 + )( + 2(1 − ) tan ) th The q quantile of the GHC(∝) distribution is given > 0, > 0 (2.2) by and the survival function is International Journal of Statistiika and Mathematika, ISSN: 2277- 2790 E-ISSN: 2249-8605, Volume 3 Issue 2 Page 78 International Journal of Statistika and Mathematika, ISSN: 2277- 2790 E-ISSN: 2249-8605, Volume 3, Issue 2, 2012 pp 77-81 ∝ = () = , where 0≤ ≤ 1 The log-likelihood of the sample is given by ((∝)) Log L = nlog(2) + − ∑ log1 + − and (.) is the inverse distribution function. For ∑ ( ( ) ) 2 log + 2 1 − tan = , ; the median and quartiles are The normal equation is respectively 3.1 − 2 tan 2 = () = + 2(1 − ) tan 2(1 + ) The maximum likelihood estimate of is the solution = 2(3 + ) of equation (3.1). It can be solved numerically by using the function nlm in the statistical software R. 3 = , 2(1 + 3) 4. First order AR process with GHC(∝) as marginal distribution: The quartile measure of skewness is obtained as In this section, we develop stationary autoregressive 3 + − 2 2(1 + 3) 2(3 + ) 2(1 + ) maximum process with GHC(∝) marginal distribution. () = 3 The autoregressive sequence {Xn} of 2(1 + 3) − 2(3 + ) GHC(∝)distributed random variables are related in the following manner: Definition 2.3 (Marshall and Olkin (1997)). Let { , ≥ 1} be a sequence of independent , . , (4.1) = identically distributed random variables with common max(, ) , . 1 − cdf F (x) and let N be a geometric random where 0 < p < 1 and { } is a sequence of i.i.d variable with parameter p such that P (N = n) = n-1 HC random variables, independent of {Xn}. p(1-p) , n=1,2,….; 0<p<1, which is independent of Xi for all ≥ 1. Also, let U=min(X1,X2,,,,, XN) and V = Theorem 4.1. {Xn} as defined by (4.1) is a stationary max(X1,X2,,,,, XN). If ℑ implies that the distribution AR(1) process with GHC (1/p) marginal distribution of U (V ) is in ℑ , then ℑ is said to be geometric- if and only if {} is distributed as HC, provided minimum stable (geometric-maximum stable). If ℑ (1/p). is both geometric-minimum and geometric-maximum Proof. From (4.1), stable, then ℑ is said to be geometric-extreme stable. () = () + (1 − ) () () Theorem 2.4. Let { , ≥ 1} and N are defined as () in definition (2.3). Then, = , ()() (i) min(X1,X2,,,,, XN) has GHC(∝ ) distribution, ∝ assuming stationarity. Let with cdf (1.3). (ii) max(X1,X2,,,,, XN) has GHC distribution. Then, Proof. Let U = min(X1,X2, …. XN) and () = V=max(X1,X2,XN). The survival function of U is given by P (U ≥ x) = P(min(X ,X ,,,,, X ) ≥ x) 1 2 N Conversely, Let (1/p) ∑ = (1 − ) (()) () ( ) ∝( ) = () () = () = tan That is, GHC(∝) distribution is geometric minimum stable. Similarly, the cdf of V is given by To prove stationarity, let (1/p) and X1 is P (V ≤ x) = P(max(X1,X2,,,,, XN) ≤ x) as defined by (4.1). = ∑ (1 − ) () () = [ + (1 − ) ()] () = , () 2 tan = which shows that the distribution is geometric 1 maximum stable. + 1 − 2tan Remark 2.5. It follows from definition (2.3) and Now, let (1/p) and Xn is as defined by theorem (2.4) that GHC(∝) distribution is geometric extreme stable.
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