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  2. Constrained least squares - Wikipedia

    en.wikipedia.org/wiki/Constrained_least_squares

    In constrained least squares one solves a linear least squares problem with an additional constraint on the solution. [ 1 ] [ 2 ] This means, the unconstrained equation X β = y {\displaystyle \mathbf {X} {\boldsymbol {\beta }}=\mathbf {y} } must be fit as closely as possible (in the least squares sense) while ensuring that some other property ...

  3. Non-negative least squares - Wikipedia

    en.wikipedia.org/wiki/Non-negative_least_squares

    In mathematical optimization, the problem of non-negative least squares (NNLS) is a type of constrained least squares problem where the coefficients are not allowed to become negative. That is, given a matrix A and a (column) vector of response variables y , the goal is to find [ 1 ]

  4. Overfitting - Wikipedia

    en.wikipedia.org/wiki/Overfitting

    A straight line can never fit a parabola. This model is too simple. In mathematical modeling, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably". [1]

  5. The problem with pulse oximeters your doctor probably doesn’t ...

    www.aol.com/news/problem-pulse-oximeters-doctor...

    Potential problems with the measurement devices have been known for decades. The FDA began to develop guidance for manufacturers to address the issues in 2013, but little has been done since.

  6. Curve fitting - Wikipedia

    en.wikipedia.org/wiki/Curve_fitting

    Curve fitting [1] [2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, [3] possibly subject to constraints. [ 4 ] [ 5 ] Curve fitting can involve either interpolation , [ 6 ] [ 7 ] where an exact fit to the data is required, or smoothing , [ 8 ] [ 9 ] in which a "smooth ...

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  8. Pearson's chi-squared test - Wikipedia

    en.wikipedia.org/wiki/Pearson's_chi-squared_test

    For a test of goodness-of-fit, df = Cats − Params, where Cats is the number of observation categories recognized by the model, and Params is the number of parameters in the model adjusted to make the model best fit the observations: The number of categories reduced by the number of fitted parameters in the distribution.

  9. Shrink-fitting - Wikipedia

    en.wikipedia.org/wiki/Shrink-fitting

    Shrink-fitting is a technique in which an interference fit is achieved by a relative size change after assembly. This is usually achieved by heating or cooling one component before assembly and allowing it to return to the ambient temperature after assembly, employing the phenomenon of thermal expansion to make a joint.

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