scalation.stat

MethodOfMoments

Related Doc: package stat

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

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

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. MethodOfMoments
  2. AnyRef
  3. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Type Members

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

    Standard functional form for parameter estimating functions

Value Members

  1. final def !=(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

    Definition Classes
    AnyRef → Any

  3. final def ==(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  5. def bernoulli(x: StatVector): 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: StatVector): 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

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  8. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  10. def exponential(x: StatVector): 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

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  12. def gamma(x: StatVector): 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: StatVector): 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[_]

    Definition Classes
    AnyRef → Any

  15. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  16. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  17. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  18. def normal(x: StatVector): 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

    Definition Classes
    AnyRef

  20. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  21. def pareto(x: StatVector): 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: StatVector): 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

    Definition Classes
    AnyRef

  24. def toString(): String

    Definition Classes
    AnyRef → Any

  25. def uniform(x: StatVector): 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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  27. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  28. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from AnyRef

Inherited from Any

Ungrouped