Question

In: Statistics and Probability

Distinguish between the Force of Mortality μ(x). the probability density function f(x) and the cumulative hazard...

Distinguish between the Force of Mortality μ(x). the probability density function f(x) and the cumulative hazard function Λ(x) of a-life-aged-x.

Solutions

Expert Solution

::::Answer:::

In actuarial science, force of mortality represents the instantaneous rate of mortality at a certain age measured on an annualized basis. It is identical in concept to failure rate, also called hazard function, in reliability theory.

In a life table, we consider the probability of a person dying from age x to x + 1, called qx. In the continuous case, we could also consider the conditional probability of a person who has attained age (x) dying between ages x and x + Δx, which is

where FX(x) is the cumulative distribution function of the continuous age-at-death random variable, X. As Δx tends to zero, so does this probability in the continuous case. The approximate force of mortality is this probability divided by Δx. If we let Δx tend to zero, we get the function for force of mortality:

Since fX(x)=F 'X(x) is the probability density function of X, and S(x) = 1 - FX(x) is the survival function, the force of mortality can also be expressed variously as:

To understand conceptually how the force of mortality operates within a population, consider that the ages, x, where the probability density function fX(x) is zero, there is no chance of dying. Thus the force of mortality at these ages is zero. The force of mortality μ(x) uniquely defines a probability density function fX(x).

The force of mortality can be interpreted as the conditional density of failure at age x, while f(x) is the unconditional density of failure at age x. The unconditional density of failure at age x is the product of the probability of survival to age x, and the conditional density of failure at age x, given survival to age x.

This is expressed in symbols as

or equivalently

In many instances, it is also desirable to determine the survival probability function when the force of mortality is known. To do this, integrate the force of mortality over the interval x to x + t

By the fundamental theorem of calculus, this is simply

Let us denote

then taking the exponent to the base e, the survival probability of an individual of age x in terms of the force of mortality is


Related Solutions

Let the continuous random variable X have probability density function f(x) and cumulative distribution function F(x)....
Let the continuous random variable X have probability density function f(x) and cumulative distribution function F(x). Explain the following issues using diagram (Graphs) a) Relationship between f(x) and F(x) for a continuous variable, b) explaining how a uniform random variable can be used to simulate X via the cumulative distribution function of X, or c) explaining the effect of transformation on a discrete and/or continuous random variable
Consider a continuous random variable X with the probability density function f X ( x )...
Consider a continuous random variable X with the probability density function f X ( x ) = x/C , 3 ≤ x ≤ 7, zero elsewhere. Consider Y = g( X ) = 100/(x^2+1). Use cdf approach to find the cdf of Y, FY(y). Hint: F Y ( y ) = P( Y <y ) = P( g( X ) <y ) =
A probability density function on R is a function f :R -> R satisfying (i) f(x)≥0...
A probability density function on R is a function f :R -> R satisfying (i) f(x)≥0 or all x e R and (ii) \int_(-\infty )^(\infty ) f(x)dx = 1. For which value(s) of k e R is the function f(x)= e^(-x^(2))\root(3)(k^(5)) a probability density function? Explain.
Suppose that the distribution of wind velocity, X, is described by the probability density function f(x)...
Suppose that the distribution of wind velocity, X, is described by the probability density function f(x) = (x/σ^2)e^-(x^2/ 2(σ^2)) , x ≥ 0. Suppose that for the distribution of wind velocity in Newcastle, measured in km/hr, σ^2 = 100. (a) In task 1, you showed that the quantile function for this distribution is given by: Q(p) = σ (−2 ln(1 − p))^(1/2), 0 ≤ p < 1 Use this quantile function to generate 100,000 random values from this distribution (when...
Suppose that the joint probability density function of ˜ (X, Y) is given by:´ f X,Y...
Suppose that the joint probability density function of ˜ (X, Y) is given by:´ f X,Y (x,y) = 4x/y3 I(0.1)(x), I (1, ∞)(y). Calculate a) P(1/2 < X < 3/4, 0 < Y ≤ 1/3). b) P(Y > 5). c) P(Y > X).
Let be the following probability density function f (x) = (1/3)[ e ^ {- x /...
Let be the following probability density function f (x) = (1/3)[ e ^ {- x / 3}] for 0 <x <1 and f (x) = 0 in any other case a) Determine the cumulative probability distribution F (X) b) Determine the probability for P (0 <X <0.5)
If a continuous random process has a probability density function f(x) = a + bx, for...
If a continuous random process has a probability density function f(x) = a + bx, for 0 < x < 5, where a and b are constants, and P(X > 3) = 0.3 Determine: The values of a and b. The cumulative distribution function F(x) P(X < 2) P(2 < X < 4)
If a continuous random process has a probability density function f(x) = a + bx, for...
If a continuous random process has a probability density function f(x) = a + bx, for 0 < x < 5, where a and b are constants, and P(X > 3) = 0.3 Determine: The values of a and b. The cumulative distribution function F(x) P(X < 2) P(2 < X < 4)
2 Consider the probability density function (p.d.f) of a continuous random variable X: f(x) = (...
2 Consider the probability density function (p.d.f) of a continuous random variable X: f(x) = ( k x3 , 0 < x < 1, 0, elsewhere, where k is a constant. (a) Find k. (b) Compute the cumulative distribution function F(x) of X. (c) Evaluate P(0.1 < X < 0.8). (d) Compute µX = E(X) and σX.
Suppose that X and Y have the following joint probability density function. f (x, y) =...
Suppose that X and Y have the following joint probability density function. f (x, y) = (3/394)*y, 0 < x < 8, y > 0, x − 3 < y < x + 3 (a)   Find E(XY). (b)   Find the covariance between X and Y.
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT