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 classJohansenAsymptoticDistributionJohansen 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 classBetaDistributionThe 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.classBinomialDistributionThe 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.classChiSquareDistributionThe Chi-square distribution is the distribution of the sum of the squares of a set of statistically independent standard Gaussian random variables.classEmpiricalDistributionAn 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.classExponentialDistributionThe 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.classFDistributionThe F distribution is the distribution of the ratio of two independent chi-squared variates.classGammaDistributionThis 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).classLogNormalDistributionA log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed.classNormalDistributionThe Normal distribution has its density a Gaussian function.classPoissonDistributionThe 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.classRayleighDistributionThe L2 norm of (x1, x2), where xi's are normal, uncorrelated, equal variance and have the Rayleigh distributions.classTDistributionThe 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.classTriangularDistributionThe triangular distribution is a continuous probability distribution with lower limit a, upper limit b and mode c, where a < b and a ≤ c ≤ b.classTruncatedNormalDistributionThe truncated Normal distribution is the probability distribution of a normally distributed random variable whose value is either bounded below or above (or both).classWeibullDistributionThe 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 ProbabilityDistributionExponentialFamily. 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 interfaceUnivariateEVDDistribution 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 classFrechetDistributionThe Fréchet distribution is a special case (Type II) of the generalized extreme value distribution, with \(\xi>0\).classGeneralizedEVDGeneralized 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.classGeneralizedParetoDistributionGeneralized Pareto distribution (GPD) is used for modeling exceedances over (or shortfalls below) a threshold.classGumbelDistributionThe Gumbel distribution is a special case (Type I) of the generalized extreme value distribution, with \(\xi=0\).classMaximaDistributionThe 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 \(F(x)\).classMinimaDistributionThe 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 \(F(x)\).classOrderStatisticsDistributionThe asymptotic nondegenerate distributions of the r-th smallest (largest) order statistic.classReversedWeibullDistributionThe 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() -
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 doubleHypothesisTest. 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 classKolmogorovDistributionThe Kolmogorov distribution is the distribution of the Kolmogorov-Smirnov statistic.classKolmogorovOneSidedDistributionCompute 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}}classKolmogorovTwoSamplesDistributionCompute 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 classJarqueBeraDistributionJarque-Bera distribution is the distribution of the Jarque-Bera statistics, which measures the departure from normality.classShapiroWilkDistributionShapiro-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 classFisherExactDistributionFisher'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 classWilcoxonRankSumDistributionCompute the exact distribution of the Wilcoxon rank sum test statistic.classWilcoxonSignedRankDistributionCompute 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 classADFAsymptoticDistributionThis class computes the asymptotic distribution of the Augmented Dickey-Fuller (ADF) test statistic.classADFAsymptoticDistribution1Deprecated.use insteadADFAsymptoticDistributionclassADFDistributionThis represents an Augmented Dickey Fuller distribution.classADFFiniteSampleDistributionThis class computes the finite sample distribution of the Augmented Dickey-Fuller (ADF) test statistics.
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