• 제목/요약/키워드: $x^2$ Distribution

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X선촬영시(線撮影時) 연부조직(軟部組織) 두께에 따른 선량분포(線量分布)에 관(關)한 연구(硏究) (A Study on the Distribution of X-ray according to the Thickness of Soft Tissue in Radiography)

  • 박성옥
    • 대한방사선기술학회지:방사선기술과학
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    • 제11권2호
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    • pp.3-15
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    • 1988
  • When X-rays were projected into a patient, there occured the phenomena such as penetration, absorption and scattering etc. The penetrating rays were recorded on films as X-ray image used for diagnosis but scattered rays caused the radiation hazard both to the patient, specialist and technicians. The soft tissue includes many organs which are sensitive to the radiation and in may occupy $40{\sim}50%$ of body weight. Therefore X-rays should be carefully projected to the patient and it is strongly recommended to analyse the distribution of X-rays, when ever the patient is exposed to X-rays. In this study, the distribution of X-ray according to the thickness, the radiation field and the tube voltages (kVp) in soft tissue, the following results were obtained: 1. Total transmitted rays which kept the step with X-ray tube voltage (kVp) increased in proportion to the increasing of X-ray tube voltage. 2. The scattered ray rate in the total transmitted ray was not significantly found with X-ray tube voltage. 3. The affecting factors of the scattered ray rate in total transmitted ray were shown through the radiation field and the thickness. 4. The dose of scattered ray by the angle was observed more in direction of primary ray ($0^{\circ}$) and back scattering ($160^{\circ}$) than in direction of $90^{\circ}$. 5. The more the distance from phantom to the patient should be less distribution of scattered ray.

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RECURRENCE RELATION FOR QUOTIENTS OF THE POWER DISTRIBUTION BY RECORD VALUES

  • Lee, Min-Young;Chang, Se-Kyung
    • Korean Journal of Mathematics
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    • 제12권1호
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    • pp.15-22
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    • 2004
  • In this paper we establish some recurrence relations satisfied by quotient moments of upper record values from the power distribution. Let {$X_n$, $n{\geq}1$} be a sequence of independent an identically distributed random variables with a common continuous distribution function(cdf) $F(x)$ and probability density function(pdf) $f(x)$. Let $Y_n=max\{X_1,X_2,{\cdots},X_n\}$ for $n{\geq}1$. We say $X_j$ is an upper record value of {$X_n$, $n{\geq}1$}, if $Y_j$ > $Y_{j-1}$, $j$ > 1. The indices at which the upper record values occur are given by the record times {$u(n)$}, $n{\geq}1$, where $u(n)=min\{j{\mid}j>u(n-1),X_j>X_{u(n-1)},n{\geq}2\}$ and $u(1)=1$. Suppose $X{\in}POW(0,1,{\theta})$ then $$E\left(\frac{X^r_{u(m)}}{X^{s+1}_{u(n)}}\right)=\frac{\theta}{s}E\left(\frac{X^r_{u(m)}}{X^s_{u(n-1)}}\right)+\frac{(s-\theta)}{s}E\left(\frac{X^r_{u(m)}}{X^s_{u(n)}\right)\;and\;E\left(\frac{X^{r+1}_{u(m)}}{X^s_{u(n)}}\right)=\frac{\theta}{n+1}\left[E\left(\frac{X^{r+1}_{u(m-1)}}{X^s_{u(n+1)}}\right)-E\left(\frac{X^{r+1}_{u(m)}}{X^s_{u(n-1)}}\right)+\frac{r+1}{\theta}E\left(\frac{X^r_{u(m)}}{X^s_{u(n)}}\right)\right]$$.

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A SKEWED GENERALIZED t DISTRIBUTION

  • NADARAJAH SARALEES
    • Journal of the Korean Statistical Society
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    • 제34권4호
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    • pp.311-329
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    • 2005
  • Skewed t distributions have attracted significant attention in the last few years. In this paper, a generalization - referred to as the skewed generalized t distribution - with the pdf f(x) = 2g(x)G(${\lambda}x$) is introduced, where g(${\cdot}$) and G (${\cdot}$) are taken, respectively, to be the pdf and the cdf of the generalized t distribution due to McDonald and Newey (1984, 1988). Several particular cases of this distribution are identified and various representations for its moments derived. An application is provided to rainfall data from Orlando, Florida.

CHARACTERIZATIONS OF THE POWER FUNCTION DISTRIBUTION BY THE INDEPENDENCE OF THE LOWER RECORD VALUES

  • Chang, Se-Kyung
    • Journal of applied mathematics & informatics
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    • 제25권1_2호
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    • pp.541-550
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    • 2007
  • This paper presents characterizations of the power distribution with the parameter $\beta=1$ by the independence of the lower record values. We prove $X\;{\in}\;POW({\alpha},\;1)$ for ${\alpha}\;>\;0$, if and only if $\frac{X_{L(n)}}{X_{L(m)}}$ and $X_{L(m)}$ for $1\;{\leq}\;m\;<\;n$ are independent. And we prove that $X\;{\in}\;POW({\alpha},\;1)$ for ${\alpha}\;>\;0$, if and only if $\frac{X_{L(m)}-X_{L(m+1)}}{X_{L(m)}}$ and $X_{L(m)$ for $m\;{\geq}\;1$ are independent or $\frac{X_{L(m)}-X_{L(m+1)}}{X_{L(m+1)}}$ and $X_{L(m)}$ for $m\;{\geq}\;1$ are independent.

RECURRENCE RELATIONS FOR QUOTIENT MOMENTS OF THE WEIBULL DISTRIBUTION BY RECORD VALUES

  • Chang, Se-Kyung
    • Journal of applied mathematics & informatics
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    • 제23권1_2호
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    • pp.471-477
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    • 2007
  • In this paper we establish some recurrence relations satisfied by the quotient moments of the upper record values from the Weibull distribution. Suppose $X{\in}WEI({\lambda})\;then\;E(\frac {X^\tau_U(m)} {X^{s+1}_{U(n)}})=\frac{1}{(s-\lambda+1)}E(\frac {X^\tau_U(m)}{X^{s-\lambda+1}_{U(n-1)}})-\frac{1}{(s-\lambda+1)}+E(\frac{X^\tau_U(m)}{X^{s-\lambda+1}_{U(n)}})\;and\;E(\frac {X^{\tau+1}_{U(m)}}{X^s_{U(n)}})=\frac{1}{(r+\lambda+1)}E(\frac{X^{\tau+\lambda+1}_{U(m)}}{X^s_{U(n-1)}})-\frac{1}{(\tau+\lambda+1)}E(\frac{X^{\tau+\lambda+1}_{U(m-1)}}{X^s_{U(n-1)}})$.

RECURRENCE RELATIONS FOR QUOTIENT MOMENTS OF THE PARETO DISTRIBUTION BY RECORD VALUES

  • Lee, Min-Young;Chang, Se-Kyung
    • 한국수학교육학회지시리즈B:순수및응용수학
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    • 제11권1호
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    • pp.97-102
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    • 2004
  • In this paper we establish some recurrence relations satisfied by quotient moments of upper record values from the Pareto distribution. Let {$X_n,n\qeq1$}be a sequence of independent and identically distributed random variables with a common continuous distribution function(cdf) F($chi$) and probability density function(pdf) f($chi$). Let $Y_n\;=\;mas{X_1,X_2,...,X_n}$ for $ngeq1$. We say $X_{j}$ is an upper record value of {$X_{n},n\geq1$}, if $Y_{j}$$Y_{j-1}$,j>1. The indices at which the upper record values occur are given by the record times ${u( n)}n,\geq1$, where u(n) = min{j|j >u(n-l), $X_{j}$$X_{u(n-1)}$,n\qeq2$ and u(l) = 1. Suppose $X{\epsilon}PAR(\frac{1}{\beta},\frac{1}{\beta}$ then E$(\frac{{X^\tau}}_{u(m)}}{{X^{s+1}}_{u(n)})\;=\;\frac{1}{s}E$ E$(\frac{{X^\tau}}_{u(m)}{{X^s}_{u(n-1)}})$ - $\frac{(1+\betas)}{s}E(\frac{{X^\tau}_{u(m)}}{{X^s}_{u(n)}}$ and E$(\frac{{X^{\tau+1}}_{u(m)}}{{X^s}_{u(n)}})$ = $\frac{1}{(r+1)\beta}$ [E$(\frac{{X^{\tau+1}}}_u(m)}{{X^s}_{u(n-1)}})$ - E$(\frac{{X^{\tau+1}}_u(m)}}{{X^s}_{u(n-1)}})$ - (r+1)E$(\frac{{X^\tau}_{u(m)}}{{X^s}_{u(n)}})$]

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A Distribution of Terminal Time Value and Running Maximum of Two-Dimensional Brownian Motion with an Application to Barrier Option

  • Lee, Hang-Suck
    • 한국통계학회:학술대회논문집
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    • 한국통계학회 2003년도 추계 학술발표회 논문집
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    • pp.73-78
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    • 2003
  • This presentation derives a distribution function of the terminal value and running maximum of two-dimensional Brownian motion {X(t) = (X$_1$(t), X$_2$(T))', t > 0}. One random variable of the joint distribution is the terminal time value of the Brownian motion {X$_1$(t), t > 0}. The other random variable is the partial-time running maximum of the Brownian motion {X$_2$(t), t > 0}. With this distribution function, this presentation also derives an explicit pricing formula for a barrier option whose monitoring period of the option starts at an arbitrary date and ends at another arbitrary date before maturity.

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Cu-Zn 훼라이트의 자기적 성질 (Magnetic Properties of Cu-Zn Ferrites)

  • 이충섭;이찬영;김철성;지상희
    • 한국자기학회지
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    • 제3권1호
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    • pp.18-22
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    • 1993
  • $Cu_{x}Zn_{1-x}Fe_{2}O_{4}(0{\leq}x{\leq}1)$의 이온분포 및 자기적 성질을 X-선 회절법과 $M\"{o}ssbauer$ 분광법으로 연구하였다. 결정구조는 $0{\leq}x{\leq}0.9$의 영역에서 입방 스피넬이다. $ZnFe_{2}O_{4}$의 이온분포는 ${(Zn_{1-x}Fe_{x})}_{A}{[Zn_{x}Fe_{2-x}]}_{B}O_{4}$:x=0.1 이다. Curie 온도 이하의 $M\"{o}ssbauer$ spectrum에서 $Fe^{3+}$ 이온의 분포상태를 $0{\leq}x{\leq}1$의 전 영역에서 얻었다. Cu의 농도 x가 증가함에 따라서 사면체자리에 들어가는 $Fe^{3+}$ 이온의 수가 증가하고 Cu-Zn 훼라이트의 Curie 온도가 높아진다.

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Extreme Value of Moving Average Processes with Negative Binomial Noise Distribution

  • Park, You-Sung
    • Journal of the Korean Statistical Society
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    • 제21권2호
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    • pp.167-177
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    • 1992
  • In this paper, we investigate the limiting distribution of $M_n = max (X_1, X-2, \cdots, X_n)$ in the infinite moving average process ${X_t = \sum c_i Z_{t-i}}$ generated from i.i.d. negative binomial variables $Z_i$'s. While no limit result is possible, nonetheless asymptotic bounds are derived. We also present the tail behavior of $X_t$, i.e., weighted sum of i.i.d. random variables. This continues a study made by Rootzen (1986) for discrete innovation sequences.

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