Uses of Interface
dev.nm.stat.distribution.univariate.ProbabilityDistribution
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Uses of ProbabilityDistribution in dev.nm.stat.cointegration
Classes in dev.nm.stat.cointegration that implement ProbabilityDistribution Modifier and Type Class Description class
JohansenAsymptoticDistribution
Johansen provides the asymptotic distributions of the two hypothesis testings (Eigen and Trace tests), each for 5 different trend types. -
Uses of ProbabilityDistribution in dev.nm.stat.distribution.univariate
Classes in dev.nm.stat.distribution.univariate that implement ProbabilityDistribution Modifier and Type Class Description class
BetaDistribution
The beta distribution is the posterior distribution of the parameter p of a binomial distribution after observing α - 1 independent events with probability p and β - 1 with probability 1 - p, if the prior distribution of p is uniform.class
BinomialDistribution
The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p.class
ChiSquareDistribution
The Chi-square distribution is the distribution of the sum of the squares of a set of statistically independent standard Gaussian random variables.class
EmpiricalDistribution
An empirical cumulative probability distribution function is a cumulative probability distribution function that assigns probability 1/n at each of the n numbers in a sample.class
ExponentialDistribution
The exponential distribution describes the times between events in a Poisson process, a process in which events occur continuously and independently at a constant average rate.class
FDistribution
The F distribution is the distribution of the ratio of two independent chi-squared variates.class
GammaDistribution
This gamma distribution, when k is an integer, is the distribution of the sum of k independent exponentially distributed random variables, each of which has a mean of θ (which is equivalent to a rate parameter of θ-1).class
LogNormalDistribution
A log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed.class
NormalDistribution
The Normal distribution has its density a Gaussian function.class
PoissonDistribution
The Poisson distribution (or Poisson law of small numbers) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event.class
RayleighDistribution
The L2 norm of (x1, x2), where xi's are normal, uncorrelated, equal variance and have the Rayleigh distributions.class
TDistribution
The Student t distribution is the probability distribution of t, where \[ t = \frac{\bar{x} - \mu}{s / \sqrt N} \] \(\bar{x}\) is the sample mean; μ is the population mean; s is the square root of the sample variance; N is the sample size; The importance of the Student's distribution is when (as in nearly all practical statistical work) the population standard deviation is unknown and has to be estimated from the data.class
TriangularDistribution
The triangular distribution is a continuous probability distribution with lower limit a, upper limit b and mode c, where a < b and a ≤ c ≤ b.class
TruncatedNormalDistribution
The truncated Normal distribution is the probability distribution of a normally distributed random variable whose value is either bounded below or above (or both).class
WeibullDistribution
The Weibull distribution interpolates between the exponential distribution k = 1 and the Rayleigh distribution (k = 2), where k is the shape parameter. -
Uses of ProbabilityDistribution in dev.nm.stat.distribution.univariate.exponentialfamily
Methods in dev.nm.stat.distribution.univariate.exponentialfamily that return ProbabilityDistribution Modifier and Type Method Description ProbabilityDistribution
ExponentialFamily. getDistribution(Vector theta)
Construct a probability distribution in the exponential family. -
Uses of ProbabilityDistribution in dev.nm.stat.evt.evd.univariate
Subinterfaces of ProbabilityDistribution in dev.nm.stat.evt.evd.univariate Modifier and Type Interface Description interface
UnivariateEVD
Distribution of extreme values (e.g., maxima, minima, or other order statistics).Classes in dev.nm.stat.evt.evd.univariate that implement ProbabilityDistribution Modifier and Type Class Description class
FrechetDistribution
The Fréchet distribution is a special case (Type II) of the generalized extreme value distribution, with \(\xi>0\).class
GeneralizedEVD
Generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families also known as type I, II and III extreme value distributions.class
GeneralizedParetoDistribution
Generalized Pareto distribution (GPD) is used for modeling exceedances over (or shortfalls below) a threshold.class
GumbelDistribution
The Gumbel distribution is a special case (Type I) of the generalized extreme value distribution, with \(\xi=0\).class
MaximaDistribution
The distribution of \(M\), where \(M=\max(x_1,x_2,...,x_n)\) and \(x_i\)'s are iid samples drawn from of a random variable \(X\) with cdf \(F(x)\).class
MinimaDistribution
The distribution of \(M\), where \(M=\min(x_1,x_2,...,x_n)\) and \(x_i\)'s are iid samples drawn from of a random variable \(X\) with cdf \(F(x)\).class
OrderStatisticsDistribution
The asymptotic nondegenerate distributions of the r-th smallest (largest) order statistic.class
ReversedWeibullDistribution
The Reversed Weibull distribution is a special case (Type III) of the generalized extreme value distribution, with \(\xi<0\).Constructors in dev.nm.stat.evt.evd.univariate with parameters of type ProbabilityDistribution Constructor Description MaximaDistribution(ProbabilityDistribution dist, int nIIDs)
Create an instance with the probability distribution of \(X\), and the number of iid samples to be drawn.MinimaDistribution(ProbabilityDistribution dist, int nIIDs)
OrderStatisticsDistribution(ProbabilityDistribution dist, int nIIDs, int order)
Create an instance with the probability distribution of \(X\), the number of iid samples to be drawn, and the order statistic. -
Uses of ProbabilityDistribution in dev.nm.stat.hmm.mixture.distribution
Methods in dev.nm.stat.hmm.mixture.distribution that return ProbabilityDistribution Modifier and Type Method Description ProbabilityDistribution[]
BetaMixtureDistribution. newDistributions()
ProbabilityDistribution[]
BinomialMixtureDistribution. newDistributions()
ProbabilityDistribution[]
ExponentialMixtureDistribution. newDistributions()
ProbabilityDistribution[]
GammaMixtureDistribution. newDistributions()
ProbabilityDistribution[]
LogNormalMixtureDistribution. newDistributions()
ProbabilityDistribution[]
MixtureDistribution. newDistributions()
Get the distributions (possibly differently parameterized) for all states.ProbabilityDistribution[]
NormalMixtureDistribution. newDistributions()
ProbabilityDistribution[]
PoissonMixtureDistribution. newDistributions()
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Uses of ProbabilityDistribution in dev.nm.stat.random.rng.univariate
Constructors in dev.nm.stat.random.rng.univariate with parameters of type ProbabilityDistribution Constructor Description InverseTransformSampling(ProbabilityDistribution distribution)
Construct a random number generator to sample from a distribution.InverseTransformSampling(ProbabilityDistribution distribution, RandomLongGenerator uniform)
Construct a random number generator to sample from a distribution. -
Uses of ProbabilityDistribution in dev.nm.stat.test
Methods in dev.nm.stat.test with parameters of type ProbabilityDistribution Modifier and Type Method Description static double
HypothesisTest. oneSidedPvalue(ProbabilityDistribution F, double x)
The one-sided p-value is the probability of observing a test statistic at least as extreme as the one observed. -
Uses of ProbabilityDistribution in dev.nm.stat.test.distribution.kolmogorov
Classes in dev.nm.stat.test.distribution.kolmogorov that implement ProbabilityDistribution Modifier and Type Class Description class
KolmogorovDistribution
The Kolmogorov distribution is the distribution of the Kolmogorov-Smirnov statistic.class
KolmogorovOneSidedDistribution
Compute the probability that F(x) is dominated by the upper confidence contour, for all x: Pn(ε) = Pr{F(x) < min{Fn(x) + ε, 1}}class
KolmogorovTwoSamplesDistribution
Compute the p-values for the generalized (conditionally distribution-free) Smirnov homogeneity test.Constructors in dev.nm.stat.test.distribution.kolmogorov with parameters of type ProbabilityDistribution Constructor Description KolmogorovSmirnov1Sample(double[] sample, ProbabilityDistribution F, KolmogorovSmirnov.Side side)
Construct a one-sample Kolmogorov-Smirnov test. -
Uses of ProbabilityDistribution in dev.nm.stat.test.distribution.normality
Classes in dev.nm.stat.test.distribution.normality that implement ProbabilityDistribution Modifier and Type Class Description class
JarqueBeraDistribution
Jarque-Bera distribution is the distribution of the Jarque-Bera statistics, which measures the departure from normality.class
ShapiroWilkDistribution
Shapiro-Wilk distribution is the distribution of the Shapiro-Wilk statistics, which tests the null hypothesis that a sample comes from a normally distributed population. -
Uses of ProbabilityDistribution in dev.nm.stat.test.distribution.pearson
Classes in dev.nm.stat.test.distribution.pearson that implement ProbabilityDistribution Modifier and Type Class Description class
FisherExactDistribution
Fisher's exact test distribution is, as its name states, exact, and can therefore be used regardless of the sample characteristics. -
Uses of ProbabilityDistribution in dev.nm.stat.test.rank.wilcoxon
Classes in dev.nm.stat.test.rank.wilcoxon that implement ProbabilityDistribution Modifier and Type Class Description class
WilcoxonRankSumDistribution
Compute the exact distribution of the Wilcoxon rank sum test statistic.class
WilcoxonSignedRankDistribution
Compute the exact distribution of the Wilcoxon signed rank test statistic. -
Uses of ProbabilityDistribution in dev.nm.stat.test.timeseries.adf
Classes in dev.nm.stat.test.timeseries.adf that implement ProbabilityDistribution Modifier and Type Class Description class
ADFAsymptoticDistribution
This class computes the asymptotic distribution of the Augmented Dickey-Fuller (ADF) test statistic.class
ADFAsymptoticDistribution1
Deprecated.use insteadADFAsymptoticDistribution
class
ADFDistribution
This represents an Augmented Dickey Fuller distribution.class
ADFFiniteSampleDistribution
This class computes the finite sample distribution of the Augmented Dickey-Fuller (ADF) test statistics.
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