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Student t and Fisher F CDFs are continuous and strictly increasing on their supports
Statement
Assume the Axiom of Choice. For every , the CDF of is continuous and strictly increasing on , is symmetric about zero, and has limits and at and . For every , the CDF of is continuous on , is zero on , and is strictly increasing on with limit at . Consequently, for each each law has exactly one -quantile on its support.
Facts & Assumptions
Given: The Axiom of Choice, positive degrees of freedom, and the laws and .
The Axiom of Choice covers the standard-normal law and implies the Countable Choice used in the normal-density supplier. The Axiom of Choice The Axiom of Choice implies countable choice
A standard normal has the strictly positive continuous density with total mass one. Standard normal and normal laws The standard normal density has total mass one The exponential function is smooth and
The CDF of a real random variable is . Cumulative distribution function of a real random variable
Positive-degree chi-square variables are positive almost surely and have CDFs continuous and strictly increasing on . Positive-degree chi-square laws have a positive density and regular CDF
The t and F laws are ratios of independent normal and chi-square variables with positive degrees of freedom. Student t and Fisher F laws
Dominated convergence passes limits through integrals with an integrable majorant. Dominated convergence
Independent random elements have product joint law; Tonelli gives the iterated integral for nonnegative functions on the finite product spaces. Independent random elements have product joint law For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
Probabilities of increasing unions are the limits of the probabilities. Continuity from below for measures
A continuous real function on an interval takes every intermediate value. Intermediate value theorem, by bisection with a canonical left-half rule: a continuous function on takes every value between and
Proof
Given: The hypotheses in the Statement.
Let be the standard-normal CDF. For , [F1]–[F2] give . On every bounded interval is bounded, so there and is continuous. The events and increase to the whole space as integer ; [F7] and total mass one give the CDF limits and . Thus is continuous and strictly increasing on .
For , because its ratio is positive almost surely. For , conditioning on gives . If , continuity of the inner chi-square CDF and [F5] give continuity of this integral. If , strict increase of the inner CDF on and positivity of give a strict inequality between the two integrals. Dominated convergence as and gives limits and . Hence the F CDF is continuous on and strictly increasing on .
On the product space defining , condition on . Independence and [F6] give . If , the integrands converge pointwise and are bounded by , so [F5] makes . If , then for every by step 1.1, and [F3] says this is a full-measure set; integrating gives . Dominated convergence as gives limits and .
The law is symmetric: replacing by preserves the product law in [F4] and sends to . Continuity from step 2.1 therefore gives and . For either law and any , the CDF limits and continuity give points in its support with ; [F8] applied on gives a point with CDF value , and strict increase gives uniqueness. These unique points are the stated quantiles. In particular, for , because and the CDF is strictly increasing.
Depends on
- The Axiom of Choice
- Cumulative distribution function of a real random variable
- Independent random elements
- Standard normal and normal laws
- The Axiom of Choice implies countable choice
- The standard normal density has total mass one
- Positive-degree chi-square laws have a positive density and regular CDF
- Student t and Fisher F laws
- The exponential function is smooth and $(\exp)'=\exp$
- Continuity from below for measures
- Dominated convergence
- Independent random elements have product joint law
- Intermediate value theorem, by bisection with a canonical left-half rule: a continuous function on $[a,b]$ takes every value between $f(a)$ and $f(b)$
- For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique
- Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
Used by
- The one-sample Hotelling F test has exact size alpha Corollary
- Independent Rademacher errors need not give the Gaussian regression t pivot Counterexample
- A skewed finite-replicate law defeats a nominal Student interval Example
- A two-dimensional Hotelling statistic and its exact F cutoff Example
- An exact unknown-variance t interval for a four-point sample Example
- Exact unknown-variance normal t interval Example
- Exact confidence and prediction intervals in Gaussian regression Theorem
- Exact t and partial F inference in full-rank Gaussian regression Theorem
- Normal sample mean and residual are independent, giving the exact t pivot Theorem
- QR decomposition gives exact t and F pivots in the fixed Gaussian linear model Theorem
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Sources
- Dmitry Panchenko, MIT 18.443 Statistics for Applications, Fall 2003, Lectures 16–17 (standard reference, not scraped)
- Joshua M. Tebbs, STAT 512 Lecture Notes, Chapter 8, §8.7.1 (standard reference, not scraped)
- Yen-Chi Chen, UW STAT 512 Lecture 8: Linear Models and Multivariate Normal Distributions (standard reference, not scraped)