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scalation.stat

MethodOfMoments

object MethodOfMoments

The MethodOfMoments object provides methods for estimating parameters for popular probability distributions using the Method of Moments (MOM). The main alternative is to use Maximum Likelihood Estimators (MLE).

See also

www.math.uah.edu/stat/point/Moments.html

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Type Members

  1. type ParamFunction = (VectorD) ⇒ Array[Double]

    Standard functional form for parameter estimating functions

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def bernoulli(x: VectorD): Array[Double]

    Estimate the parameter 'p' for the Bernoulli distribution.

    Estimate the parameter 'p' for the Bernoulli distribution.

    x

    the statistical data vector

  6. def beta(x: VectorD): Array[Double]

    Estimate the parameters 'a' (alpha) and 'b' (beta) for the Beta distribution.

    Estimate the parameters 'a' (alpha) and 'b' (beta) for the Beta distribution.

    x

    the statistical data vector

  7. def clone(): AnyRef
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    @native() @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean
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  9. def equals(arg0: Any): Boolean
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  10. def exponential(x: VectorD): Array[Double]

    Estimate the parameter 'mu' for the Exponential distribution.

    Estimate the parameter 'mu' for the Exponential distribution.

    x

    the statistical data vector

  11. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] )
  12. def gamma(x: VectorD): Array[Double]

    Estimate the parameters 'a' (alpha) and 'b' (beta) for the Gamma distribution.

    Estimate the parameters 'a' (alpha) and 'b' (beta) for the Gamma distribution.

    x

    the statistical data vector

  13. def geometric(x: VectorD): Array[Double]

    Estimate the parameter 'p' for the Geometric distribution.

    Estimate the parameter 'p' for the Geometric distribution.

    x

    the statistical data vector

  14. final def getClass(): Class[_]
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    @native()
  15. def hashCode(): Int
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  16. final def isInstanceOf[T0]: Boolean
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  17. final def ne(arg0: AnyRef): Boolean
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  18. def normal(x: VectorD): Array[Double]

    Estimate the parameters 'mu' and 'sigma2' for the Normal distribution.

    Estimate the parameters 'mu' and 'sigma2' for the Normal distribution.

    x

    the statistical data vector

  19. final def notify(): Unit
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    @native()
  20. final def notifyAll(): Unit
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  21. def pareto(x: VectorD): Array[Double]

    Estimate the parameters 'a' and 'b' for the Pareto distribution.

    Estimate the parameters 'a' and 'b' for the Pareto distribution.

    x

    the statistical data vector

  22. def poisson(x: VectorD): Array[Double]

    Estimate the parameter 'mu' for the Poisson distribution.

    Estimate the parameter 'mu' for the Poisson distribution.

    x

    the statistical data vector

  23. final def synchronized[T0](arg0: ⇒ T0): T0
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  24. def toString(): String
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  25. def uniform(x: VectorD): Array[Double]

    Estimate the parameters 'a' and 'b' for the Uniform distribution.

    Estimate the parameters 'a' and 'b' for the Uniform distribution.

    x

    the statistical data vector

  26. final def wait(): Unit
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    @throws( ... )
  27. final def wait(arg0: Long, arg1: Int): Unit
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  28. final def wait(arg0: Long): Unit
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