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Unformatted text preview: Summary of Lecture Notes  ACTSC 232, Winter 2010 Part 1  Survival Distributions and Life Tables 1.1 Future Lifetime Random Variables (a) Let ( x ) denote a life or person aged x ( ≥ 0) at time 0 or at the current time. (b) Let T x denote the timeuntildeath of ( x ). That is to say that ( x ) will die at age x + T x or will die in T x years or T x is the death time of ( x ). The random variable T x is called the future lifetime of a life ( x ). Note that T x > 0 is a continuous random variable. (c) The distribution function (d.f.) of T x is denoted by F x ( t ) = Pr { T x ≤ t } , t ≥ . The survival distribution (s.f.) of T x is denoted by S x ( t ) = Pr { T x > t } = 1 F x ( t ) , t ≥ . The probability density function (p.d.f.) of T x is denoted by f x ( t ) = d dx F x ( t ) = d dx S x ( t ) , t ≥ . (d) Let T denote the age at the death of a newborn life or the future lifetime of (0), or the age at the death of ( x ) or the future lifetime of ( x ) from his birth. Note that T > 0 is a continuous random variable. When we view T as the age at the death of ( x ) or the future lifetime of ( x ) from his birth, then T = x + T x and T x is as a conditional random variable conditioning on T > x . In this course, we explain T x in this way. (e) The distribution function (d.f.) of T is denoted by F ( x ) = Pr { T ≤ x } . The survival distribution (s.f.) of T is denoted by S ( x ) = 1 F ( x ) = Pr { T > x } . The probability density function (p.d.f.) of X is denoted by f ( x ) = d dx F ( x ) = d dx S ( x ) . 1 (f) Relationships between the distribution of T x and the distribution of T : Pr { T x ≤ t } = Pr { T ≤ x + t  T > x } , Pr { T x > t } = Pr { T > x + t  T > x } . That means F x ( t ) = F ( x + t ) F ( x ) 1 F ( x ) = S ( x ) S ( x + t ) S ( x ) S x ( t ) = S ( x + t ) S ( x ) . (g) Review of the properties of distribution and survival functions: Let the df and sf of a r.v. Y be F ( x ) = Pr { Y ≤ x } and S ( x ) = Pr { Y > x } . i. 0 ≤ F ( x ) ≤ 1 (0 ≤ S ( x ) ≤ 1). ii. F ( x ) ( S ( x )) is a nondecreasing (nonincreasing) and rightcontinuous function. iii. lim t →∞ F ( x ) = 1 and lim t →∞ F ( x ) = 0 (lim t →∞ S ( x ) = 0 and lim t →∞ S ( x ) = 1). iv. For any a < b , Pr { a < Y ≤ b } = F ( b ) F ( a ) = S ( a ) S ( b ) . v. If F ( x ) is continuous, then for any a < b , Pr { a < Y ≤ b } = Pr { a < Y < b } = Pr { a ≤ Y ≤ b } = Pr { a ≤ Y ≤ b } = F ( b ) F ( a ) = S ( a ) S ( b ) . vi. If Y is limited or bounded from above, say Y ≤ ω , then F ( x ) = 1 , x ≥ ω S ( x ) = 0 , x ≥ ω. Note that if Y represents future lifetime, the ω is called the limiting age. vii. The conditions (i)(iii) are sufficient and necessary for a function to be a distri bution (survival) function of a random variable....
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This note was uploaded on 03/20/2011 for the course ACTSC 232 taught by Professor Matthewtill during the Summer '08 term at Waterloo.
 Summer '08
 MATTHEWTILL

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