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Density inversion for a triangular characteristic function
Example
Assume AC. The triangular function is the characteristic function of the probability density This value makes f continuous at zero.
Facts & Assumptions
Given: The hypotheses and conventions in the example.
The characteristic function integrates the exponential componentwise. Characteristic function of a real random variable.
Inversion of an already known integrable characteristic function gives a continuous density. Density inversion from an integrable characteristic function.
Nonnegative Borel densities define measures. The indefinite integral of a nonnegative measurable function is a measure.
Compact primitive increments evaluate derivative integrals. The second fundamental theorem: if is differentiable on with and is integrable, then .
The compact bridge is available under countable choice. A bounded Riemann integrable function on a closed bounded interval is Lebesgue measurable and has the same integral.
AC covers the bridge and density inversion. The Axiom of Choice.
The sine and cosine derivatives give their real primitives. The derivatives of sine and cosine are cosine and minus sine.
Compact integration by parts applies to continuously differentiable factors. If are differentiable on with integrable, then .
Nonnegative tests against a density integrate the product. Integrating against a density agrees with integrating the product.
Characteristic functions are continuous, bounded by one and normalized at zero. Basic properties of characteristic functions.
Nonnegative expanding compact truncations recover their full integral. Monotone convergence for the integral.
Verification
First use as a density in the space variable u. It is nonnegative Borel and , so it defines a probability. Its characteristic function q has vanishing imaginary part by the oddness of . For , integration by parts with and yields All factors and derivatives are continuous on , so FTC and the bridge apply. Density integration is applied to the real and imaginary positive/negative parts. At s=0, q(0)=1, and continuity follows from the characteristic-function lemma. Thus q is nonnegative everywhere and bounded by one, with for . The primitive on , the bridge and monotone convergence give ; reflection gives the other tail. Hence q is integrable.
Apply density inversion to that probability with characteristic function q. It supplies the continuous density . This equals h everywhere: if the two continuous densities differed at y, continuity would give a small interval where their difference had one strict sign, contradicting that both densities integrate to the same interval mass. In particular . Therefore is nonnegative and integrates to one, so defines a probability. It has exactly the displayed formula and the continuous value at zero.
Density integration and the identity in step 2.1 now give This includes t=0 and both endpoints t=±1, where the value is zero; outside the closed interval it is zero as well. The density value at x=0 was fixed by continuity, not division by zero. AC is inherited from the compact bridge and density inversion. The argument applied inversion only to the known law h before establishing that the triangle is a characteristic function.
Depends on
- Characteristic function of a real random variable
- Density inversion from an integrable characteristic function
- The indefinite integral of a nonnegative measurable function is a measure
- The second fundamental theorem: if $G$ is differentiable on $[a,b]$ with $G' = f$ and $f$ is integrable, then $\int_a^b f = G(b)-G(a)$
- A bounded Riemann integrable function on a closed bounded interval is Lebesgue measurable and has the same integral
- The Axiom of Choice
- The derivatives of sine and cosine are cosine and minus sine
- If $u,v$ are differentiable on $[a,b]$ with $u',v'$ integrable, then $\int_a^b u v' = u(b)v(b)-u(a)v(a) - \int_a^b u'v$
- Integrating against a density agrees with integrating the product
- Basic properties of characteristic functions
- Monotone convergence for the integral
Used by
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Dependency tree · two levels
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Sources
- Durrett, Probability: Theory and Examples, fifth edition (standard reference, not scraped)
- Norris, Probability and Measure (standard reference, not scraped)