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# day3lm - PADP 8130 Linear Models Math Review ...

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Unformatted text preview: PADP 8130: Linear Models Math Review Angela Fer7g, Ph.D. Deriva7ve Rules Power Func7ons: Natural Log Func7ons: dn ( x ) = nx n−1 dx d 1 (log x ) = dx x Exponen7al Func7ons: dx (e ) = e x dx 1 Deriva7ve Rules Product Rule: d d d f ( x ) g( x ) = f ( x ) g( x ) + g( x ) f ( x ) dx dx dx Quo7ent Rule: d d d f ( x ) g( x ) dx f ( x ) − f ( x ) dx g( x ) = dx g( x ) g2 ( x ) Chain Rule: dz dz dy = where z = f ( y( x )) dx dy dx Matrix Algebra Matrix algebra is the algebra of arrays of numbers, which is useful for simplifying the descrip7on and handling of large amounts of data. ⎡ a11 a1k ⎢ A=⎢ ⎢ an1 ank ⎢ ⎣ ⎤ ⎥ ⎥ ⎥ ⎥ ⎦ 2 Terminology •  •  •  •  Row vector – 1xn matrix Column vector – nx1 matrix Square matrix – nxn matrix Types of square matrices: –  Symmetric: aik = aki –  Diagonal: nondiagonal elements are zero –  Scalar: diagonal matrix with same value for diagonal elements –  Iden7ty (I): diagonal matrix with 1’s down the diagonal –  Triangular: zeros above or below diagonal Algebraic Manipula7on of Matrices 1 Transpose: B = A ' iff bij = a ji , ∀i, j –  Symmetric matrix: A = A ' –  Any matrix: A = A '' Trace (square matrix): sum of diagonal elements Addi7on: C = A + B = [ aij + bij ] –  Have to have same dimensions –  Rules: A + 0 = A A - B = [ aij − bij ] A+B = B+A (A + B)' = A' + B' 3 Algebraic Manipula7on of Matrices 2 Mul7plica7on: –  Inner product of 2 vectors: a'b = [ –  Product of 2 matrices: ⎡ b1 ⎤ ⎥ = a1b1 + a2 b2 a1 a2 ] ⎢ ⎢ b2 ⎥ ⎣ ⎦ ⎡ a11 a12 ⎤ ⎡ b11 b12 b13 ⎤ ⎥ ⎥⎢ AB = ⎢ ⎢ a21 a22 ⎥ ⎢ b21 b22 b23 ⎥ ⎣ ⎦⎣ ⎦ ⎡ a11b11 + a12 b21 a11b12 + a12 b22 a11b13 + a12 b23 =⎢ ⎢ a21b11 + a22 b21 a21b12 + a22 b22 a21b13 + a22 b23 ⎣ •  Have to be conformable: [nxk][kxj]=[nxj] ⎤ ⎥ ⎥ ⎦ Algebraic Manipula7on of Matrices Example Without matrix algebra, would write: yi = β1 xi1 + β2 xi 2 + β3 xi 3 + + β j xij + ε i for each observation i. Example of usefulness of matrices: y1 ⎤ ⎡ x11 x12 x1 j ⎥⎢ x2 j y2 ⎥ ⎢ x21 x22 =⎢ ⎥⎢ ⎥ yn ⎥ ⎢ xn1 xn 2 xnj ⎦⎣ [ nx1] = [ nxj ][ jx1] + [ nx1] y = Xb + e ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ⎤⎡ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎦⎣ β1 ⎤ ⎡ ⎥⎢ β2 ⎥ ⎢ + ⎥⎢ ⎥⎢ βj ⎥ ⎢ ⎦⎣ ε1 ⎤ ⎥ ε2 ⎥ ⎥ ⎥ εn ⎥ ⎦ 4 Algebraic Manipula7on of Matrices 3 Sum of values: Let ι = vector of ones; x, y = vectors ∑x i = ι'x 1 ∑ xi = (ι 'ι )−1ι ' x n ∑ xi2 = x ' x x= ∑x y ii =x ' y Idempotent matrices •  Idempotent matrices (its square equals itself) are important in sta7s7cs 1 •  An important idempotent matrix: M o = I - n ii' •  Mo “demeans” a vector: M x = (I − 1 ιι ')x = x − 1 ιι ' x n n •  x M o x = ∑ ( xi − x )2 ' ⎛1 1⎞ ⎛ x ⎞ ⎜ n n ⎟⎛ ⎟⎜ ⎜ ⎟⎜ = ⎜ ⎟ −⎜ ⎟⎜ ⎜x ⎟ ⎜1 1 ⎟⎜ ⎝ ⎠⎜ ⎟⎝ n n O 1 n ⎝ ⎠ x1 ⎞ ⎟ ⎟ xn ⎟ ⎠ ⎛ x1 − x ⎞ ⎜ ⎟ =⎜ ⎟ ⎜ xn − x ⎟ ⎝ ⎠ 5 Other Special Matrices •  Orthogonal: A if AA ' = I •  Posi7ve Deﬁnite: A if x ' Ax > 0 •  Inverse: A −1 if A -1A = I Can Par77on Matrices ⎛ ⎜ A=⎜ ⎜ ⎜ ⎜ ⎝ a11 a12 a13 a21 a22 a23 a31 a32 a33 a 41 a42 a43 a14 ⎞ ⎟ a24 ⎟ ⎛ A11 =⎜ a34 ⎟ ⎝ A 21 ⎟ a 44 ⎟ ⎠ ⎛ a11 a12 ⎞ A12 ⎞ ⎟ where A11 = ⎜ ⎟ A 22 ⎠ ⎝ a21 a22 ⎠ 6 Determinants A determinant is a polynomial of the elements of a square matrix. a11 a12 a21 a22 a11 a12 a13 det A = A = a21 a22 a23 = a11 a31 a32 a33 det A = A = = a11a22 − a12 a21 a22 a23 a32 a33 a21 a23 a31 − a12 a33 + a13 a21 a22 a31 a32 Deriva7on of the Inverse Now that we know how to get the determinant, we can get the inverse. 2x2 case: −1 ⎛ a11 a12 ⎞ 1 ⎛ a22 − a12 ⎞ ⎜ ⎟= ⎜ ⎟ det A ⎝ − a21 a11 ⎠ ⎝ a21 a22 ⎠ Diagonal case: ⎛1 ⎞ −1 0⎟ ⎜d ⎛d 0⎞ 11 ⎜ ⎝0 d22 ⎟ ⎠ =⎜ ⎜ ⎜ ⎝ 11 0 ⎟ 1⎟ ⎟ d22 ⎠ 7 Use of the inverse •  Cannot divide a matrix; instead mul7ply by its inverse •  Example: want to solve for X AX = B A -1AX = A -1B IX = A -1B X = A -1B •  Important note: X can only be solved for if the inverse of A exists and the inverse of A exists if det A is nonzero. Rank of a matrix: How we know if det A is nonzero •  The rank of a matrix is the number of linearly independent rows (and columns) in the matrix. –  If Xa=0 only for a=0, then X the columns/rows of X are linearly independent vectors. –  Said diﬀerently, a set of vectors are linearly dependent if one of the vectors can be wriien as a linear combina7on of the others. •  Matrix A has full rank if A is an mxn matrix where m≤n and the rank of A is m. The determinant of a matrix is non- zero iﬀ it has full rank. 8 Eigenvalues •  Want to solve for a vector u (eigenvector) and a scalar λ (eigenvalue) such that Au=λu. •  The characteris7c equa7on is |A- λI|=0. Solve this equa7on for λ. •  One use of eigenvalues: the rank of a symmetric matrix if the number of non- zero eigenvalues. Puong it together # of non- zero eigenvalues = # rows of A A is full rank Determinant of A is non- zero Inverse of A exists! 9 ...
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## This note was uploaded on 03/28/2012 for the course PADP 8130 taught by Professor Fertig during the Spring '12 term at LSU.

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