Definition: A fn. f : R -> R is called convex on an interval (a,b) if f(cx + dy) \leq cf(x) + df(y) \forall x, y \in (a, b) \forall c \in (0, 1), d = 1-c. Concave is -convex. Essentially stating that the function lies on or below a line segment connecting f(a) and f(b) [or above in the case of concave]. Strictly convex: f(cx+dy) < cf(x) + df(y). Jensen's Inequality X - r.v., E|X| < infty. E|f(x)| < infty. f(E X) \leq E(f(x)). If f is strictly convex, \leq -> "less than" unless X is a constant r.v. Further theorems: (1) If f is differentiable on (a,b), f is convex <=> f' is nondecreasing on (a,b). f is strictly convex <=> f' is strictly increasing on (a,b) (2) If f is twice differentiable on (a,b) f is convex <=> f'' \geq 0 on (a,b) f is strictly convex <=> f'' > 0 on (a,b) Transformations of an r.v. Where X is an r.v. with pdf f_X(x), cdf F_X(x). Y := g(X). f_Y(y) = ? (Case 1) g is differentiable and invertible on D_X (range of X). f_Y(y) = f_X(g^{-1}(y)) * |d/dy g^{-1}(y)| Also: if monotonically increasing, F_Y(y) = F_X(g^{-1}(y)) if monotonically decreasing, F_Y(y) = 1 - F_X(g^{-1}(y)) (Case 2) g is piecewise bijective. g is bijective on D_j where D_X = \cup_{j=1}^k D_j, with D_i \cap D_j = \empty if i =/= j. (i.e. D_1...D_k is a partition of D_X) Then apply (1) through a sum. f_y(y) = \sum f_X(g_j^{-1}(y)) * |d/dy g_j^{-1}(y)| * Indicator(y in range of g_j) F_Y(y) = P(g(X) \leq y) = P(\sum_{j=1}^k g_j(X) \leq y) = \int_R f_X(x) * indicator(x : g(x) \leq y) dx = \sum_{j=1}^k \int_R f_X(x) * Indicator{x : g_j(x) \leq y} dx If g is monotonic increasing, the indicator is equivalent to x \leq g_j^{-1}(y) This gives rise to several other transformations. Ex: scale transformation ( g(x) = cx ), scale-position transformation ( g(x) = cx+d ). Def: Symmetric distribution is when f_X(x) = f_X(-x). If X is a symmetric distribution and E|X| < \infty, EX = 0. EX = \int_{-\infty}^\infty xf_X(x) dx = \int_0^\infty xf_X(x) + (-x)f_X(x) dx = 0, by symmetry and some rearrangement of the integral.