Nonlinear Estimation with Fminunc

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Nonlinear Estimation Inputs LHS and RHS

Estimate this equation:

. We have data from multiple t, and want to estimate the

, and

coefficients mainly, but also

is good as well. This is an estimation problem with 3 unknowns. t ranges from

% LHS outcome variable
ar_w = [-1.7349,-1.5559,-1.4334,-1.3080,-1.1791,-1.0462,-0.9085,-0.7652,-0.6150,-0.4564,-0.2874,-0.1052,0.0943];
% RHS t variable
ar_t = [0,3,5,7,9,11,13,15,16,19,21,23,25];
% actual parameters, estimation checks if gets actual parameters back
ar_a = [-1.8974, 0.0500];

Prediction and MSE Equations

Objective function for prediction and mean-squared-error.

v_0 = 0.5;
obj_ar_predictions = @(a) (v_0 + a(1) + a(2).*ar_t - log(1 - exp(a(1) + a(2).*ar_t)));
obj_fl_mse = @(a) mean((obj_ar_predictions(a) - ar_w).^2);

Evaluate given ar_a vectors.

ar_predict_at_actual = obj_ar_predictions(ar_a);
fl_mse_at_actual = obj_fl_mse(ar_a);
mt_compare = [ar_w', ar_predict_at_actual'];
tb_compare = array2table(mt_compare);
tb_compare.Properties.VariableNames = {'lhs-outcomes', 'evaluate-rhs-at-actual-parameters'};
disp(tb_compare);
    lhs-outcomes    evaluate-rhs-at-actual-parameters
    ____________    _________________________________

      -1.7349                    -1.2349             
      -1.5559                     -1.056             
      -1.4334                   -0.93353             
       -1.308                   -0.80813             
      -1.1791                   -0.67928             
      -1.0462                   -0.54641             
      -0.9085                   -0.40877             
      -0.7652                   -0.26546             
       -0.615                   -0.19133             
      -0.4564                   0.043211             
      -0.2874                    0.21214             
      -0.1052                    0.39429             
       0.0943                    0.59369             

Unconstrained Nonlinear Estimation

Estimation to minimize mean-squared-error.

% Estimation options
options = optimset('display','on');
% Starting values
ar_a_init = [-10, -10];
% Optimize
[ar_a_opti, fl_fval] = fminunc(obj_fl_mse, ar_a_init, options);
Local minimum found.

Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.

<stopping criteria details>

Compare results.

mt_compare = [ar_a_opti', ar_a'];
tb_compare = array2table(mt_compare);
tb_compare.Properties.VariableNames = {'estimated-best-fit', 'actual-parameters'};
disp(tb_compare);
    estimated-best-fit    actual-parameters
    __________________    _________________

          -2.3541              -1.8974     
         0.057095                 0.05     