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Consider the n × n matrix M = In-Z(Z,Z)-1Z', where Z is n × K. i. Show that M is idempotent and find its rank. ii....
9. (2 pts per part) Let A be an m x n matrix, where m >...
9. (2 pts per part) Let A be an m x n matrix, where m > n, and suppose that the rank of A is n (i.e., A has full column rank). Briefly justify your answers to each question below. a. Which two of the following statements are true? i. There are no vectors in Nul(A). ii. There is no basis for Nul(A). iii. dim(Nul(A)) = 0 iv. dim(Nul(A)) = m – n b. Are the columns of A a...
Prove that any m x n matrix A of rank k can be written as A...
Prove that any m x n matrix A of rank k can be written as A = {k=1 u;v] where {u1, ..., Uk} and {V1, ... , Vk} are linearly independent sets. By SVD, any mxn matrix A of rank k can be written as A = {k=10;U;v] where {u1,..., Uk} and {V1, ... , Vk} are orthonormal sets and 01 > 02 > ... >0k > 0. For this problem, prove without using SVD.
Exercise 5. Let C be an mxn matrix. Assume that a is an n-vector that is...
Exercise 5. Let C be an mxn matrix. Assume that a is an n-vector that is independent of the rows of C. Let em+1 denote the last column of the identity matrix of order m +1, i.e., em+1 is an m + 1-vector of zeros, where the last entry is 1. If A is the (m + 1) x n matrix show that the system of equations Ag = em+1 is compatible. Note that we have made no assumptions about...
In the context of multiple regression, define the n X n matrix M =- X(X'X)-'X'. (i)...
In the context of multiple regression, define the n X n matrix M =- X(X'X)-'X'. (i) Show that M is symmetric and idempotent. (ii) Prove that m, the diagonals of the matrix M, satisfy 0 sm s 1 for t = 1, 2, ..., n. (iii) Consider the linear model y = XB + u satisfies the Gauss-Markov Assumptions. Let û be the vector of OLS residuals. Show that Eſûù' x) = oʻM (iv) Conclude that while the errors {u:...
In the context of multiple regression, define the n X n matrix M =- X(X'X)-'X'. (i)...
In the context of multiple regression, define the n X n matrix M =- X(X'X)-'X'. (i) Show that M is symmetric and idempotent. (ii) Prove that m, the diagonals of the matrix M, satisfy 0 sm s 1 for t = 1, 2, ..., n. (iii) Consider the linear model y = XB + u satisfies the Gauss-Markov Assumptions. Let û be the vector of OLS residuals. Show that Eſûù' x) = oʻM (iv) Conclude that while the errors {u:...
4. Consider solving the linear system Ax = b, where A is an rn x n matrix with m < n (under- determined case), b...
4. Consider solving the linear system Ax = b, where A is an rn x n matrix with m < n (under- determined case), by minimizing lle subject to Ar-b. (a) Show that if A Rmxn is full (row) rank, where m n, then AA is invertible. Then show that r.-A7(AAT)-ibis a solution to Ax = b. (b) Along with part (a) and the solution aAT(AA)-b, show that l thus, z is the optimal solution to the minimization problem. and...
Hello, please help solve problem and show all work thank you. (Linear models) Suppose we have a vector of n observations Y (response), which has distribution Nn(XB.ση where x is an n × p matrix of kn...
Hello, please help solve problem and show all work thank
you.
(Linear models) Suppose we have a vector of n observations Y (response), which has distribution Nn(XB.ση where x is an n × p matrix of known values (indepedent variables), which has full column rank p, and β is a p x 1 vector of unknown parameters. The least squares estimator of ß is 4. a. Determine the distribution of β. xB. Determine the distribution of Y b. Let Y...
Consider the following algorithms int add.them (int n, int AI) index iふk; j=0; for (i =...
Consider the following algorithms int add.them (int n, int AI) index iふk; j=0; for (i = 1; n; i++) jsjtA[i]; for (i = 1; n; i++) return j+ int anysqual (int n, int A) index i,j,k.m; for ( iSn i++) for G-1:jSnj++) for (k = 1; k n: k++) for (m t= 1; m n: m++) return 1 return 0 Note: The array parameter A[ I[1 in any equal is an n x n two-dimensional array. For example when n...
Problem 2.2 n and let X ε Rnxp be a full-rank matrix, and Assume p Note that H is a square n × n ...
Please show all work in READ-ABLE way. Thank you so much in
advance.
Problem 2.2 n and let X ε Rnxp be a full-rank matrix, and Assume p Note that H is a square n × n matrix. This problem is devoted to understanding the properties H Any matrix that satisfies conditions in (a) is an orthogonal projection matriz. In this problem, we will verify this directly for the H given in (1). Let V - Im(X). (b) Show that...
In the next exercises, we consider square n X n matrices; I is the identity matrix...
In the next exercises, we consider square n X n matrices; I is the identity matrix (In MATLAB eye (n) gives a square n by n matrix). If ex is the column unit vector which components are all O's except the kth component which is equal to 1, i.e., 이".e1 = 101 0 then the identity matrix I is such that: 10 000 ei = [100 01T, , el 1000 11T. 0 0 T' 0 0 1 To generate in...
9. (2 pts per part) Let A be an m x n matrix, where m > n, and suppose that the rank of A is n (i.e., A has full column rank). Briefly justify your answers to each question below. a. Which two of the following statements are true? i. There are no vectors in Nul(A). ii. There is no basis for Nul(A). iii. dim(Nul(A)) = 0 iv. dim(Nul(A)) = m – n b. Are the columns of A a...
Prove that any m x n matrix A of rank k can be written as A = {k=1 u;v] where {u1, ..., Uk} and {V1, ... , Vk} are linearly independent sets. By SVD, any mxn matrix A of rank k can be written as A = {k=10;U;v] where {u1,..., Uk} and {V1, ... , Vk} are orthonormal sets and 01 > 02 > ... >0k > 0. For this problem, prove without using SVD.
Exercise 5. Let C be an mxn matrix. Assume that a is an n-vector that is independent of the rows of C. Let em+1 denote the last column of the identity matrix of order m +1, i.e., em+1 is an m + 1-vector of zeros, where the last entry is 1. If A is the (m + 1) x n matrix show that the system of equations Ag = em+1 is compatible. Note that we have made no assumptions about...
In the context of multiple regression, define the n X n matrix M =- X(X'X)-'X'. (i) Show that M is symmetric and idempotent. (ii) Prove that m, the diagonals of the matrix M, satisfy 0 sm s 1 for t = 1, 2, ..., n. (iii) Consider the linear model y = XB + u satisfies the Gauss-Markov Assumptions. Let û be the vector of OLS residuals. Show that Eſûù' x) = oʻM (iv) Conclude that while the errors {u:...
In the context of multiple regression, define the n X n matrix M =- X(X'X)-'X'. (i) Show that M is symmetric and idempotent. (ii) Prove that m, the diagonals of the matrix M, satisfy 0 sm s 1 for t = 1, 2, ..., n. (iii) Consider the linear model y = XB + u satisfies the Gauss-Markov Assumptions. Let û be the vector of OLS residuals. Show that Eſûù' x) = oʻM (iv) Conclude that while the errors {u:...
4. Consider solving the linear system Ax = b, where A is an rn x n matrix with m < n (under- determined case), by minimizing lle subject to Ar-b. (a) Show that if A Rmxn is full (row) rank, where m n, then AA is invertible. Then show that r.-A7(AAT)-ibis a solution to Ax = b. (b) Along with part (a) and the solution aAT(AA)-b, show that l thus, z is the optimal solution to the minimization problem. and...
Hello, please help solve problem and show all work thank
you.
(Linear models) Suppose we have a vector of n observations Y (response), which has distribution Nn(XB.ση where x is an n × p matrix of known values (indepedent variables), which has full column rank p, and β is a p x 1 vector of unknown parameters. The least squares estimator of ß is 4. a. Determine the distribution of β. xB. Determine the distribution of Y b. Let Y...
Consider the following algorithms int add.them (int n, int AI) index iふk; j=0; for (i = 1; n; i++) jsjtA[i]; for (i = 1; n; i++) return j+ int anysqual (int n, int A) index i,j,k.m; for ( iSn i++) for G-1:jSnj++) for (k = 1; k n: k++) for (m t= 1; m n: m++) return 1 return 0 Note: The array parameter A[ I[1 in any equal is an n x n two-dimensional array. For example when n...
Please show all work in READ-ABLE way. Thank you so much in
advance.
Problem 2.2 n and let X ε Rnxp be a full-rank matrix, and Assume p Note that H is a square n × n matrix. This problem is devoted to understanding the properties H Any matrix that satisfies conditions in (a) is an orthogonal projection matriz. In this problem, we will verify this directly for the H given in (1). Let V - Im(X). (b) Show that...
In the next exercises, we consider square n X n matrices; I is the identity matrix (In MATLAB eye (n) gives a square n by n matrix). If ex is the column unit vector which components are all O's except the kth component which is equal to 1, i.e., 이".e1 = 101 0 then the identity matrix I is such that: 10 000 ei = [100 01T, , el 1000 11T. 0 0 T' 0 0 1 To generate in...
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