Search results for "Probability Distribution"

showing 10 items of 263 documents

Response models for mixed binary and quantitative variables

1992

SUMMARY A number of special representations are considered for the joint distribution of qualitative, mostly binary, and quantitative variables. In addition to the conditional Gaussian models and to conditional Gaussian regression chain models some emphasis is placed on models derived from an underlying multivariate normal distribution and on models in which discrete probabilities are specified linearly in terms of unknown parameters. The possibilities for choosing between the models empirically are examined, as well as the testing of independence and conditional independence and the estimation of parameters. Often the testing of independence is exactly or nearly the same for a number of di…

Statistics and ProbabilityChain rule (probability)Applied MathematicsGeneral MathematicsMultivariate normal distributionConditional probability distributionAgricultural and Biological Sciences (miscellaneous)Discriminative modelConditional independenceJoint probability distributionStatisticsStatistics Probability and UncertaintyGeneral Agricultural and Biological SciencesConditional varianceIndependence (probability theory)MathematicsBiometrika
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Random Logistic Maps II. The Critical Case

2003

Let (X n )∞ 0 be a Markov chain with state space S=[0,1] generated by the iteration of i.i.d. random logistic maps, i.e., X n+1=C n+1 X n (1−X n ),n≥0, where (C n )∞ 1 are i.i.d. random variables with values in [0, 4] and independent of X 0. In the critical case, i.e., when E(log C 1)=0, Athreya and Dai(2) have shown that X n → P 0. In this paper it is shown that if P(C 1=1)<1 and E(log C 1)=0 then (i) X n does not go to zero with probability one (w.p.1) and in fact, there exists a 0<β<1 and a countable set ▵⊂(0,1) such that for all x∈A≔(0,1)∖▵, P x (X n ≥β for infinitely many n≥1)=1, where P x stands for the probability distribution of (X n )∞ 0 with X 0=x w.p.1. A is a closed set for (X n…

Statistics and ProbabilityCombinatoricsDiscrete mathematicsDistribution (mathematics)Multivariate random variableInitial distributionGeneral MathematicsZero (complex analysis)Random elementProbability distributionStatistics Probability and UncertaintyRandom variableMathematicsJournal of Theoretical Probability
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A Unified Approach to Likelihood Inference on Stochastic Orderings in a Nonparametric Context

1998

Abstract For data in a two-way contingency table with ordered margins, we consider various hypotheses of stochastic orders among the conditional distributions considered by rows and show that each is equivalent to requiring that an invertible transformation of the vectors of conditional row probabilities satisfies an appropriate set of linear inequalities. This leads to the construction of a general algorithm for maximum likelihood estimation under multinomial sampling and provides a simple framework for deriving the asymptotic distribution of log-likelihood ratio tests. The usual stochastic ordering and the so called uniform and likelihood ratio orderings are considered as special cases. I…

Statistics and ProbabilityCombinatoricsIndependent and identically distributed random variablesLinear inequalityTransformation (function)Likelihood-ratio testAsymptotic distributionApplied mathematicsConditional probability distributionStatistics Probability and UncertaintyStochastic orderingStatistical hypothesis testingMathematicsJournal of the American Statistical Association
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On Association Models Defined over Independence Graphs

1998

Conditions on joint distributions are given under which two variables will be conditionally associated whenever an independence graph does not imply a corresponding conditional independence statement. To this end the notions of parametric cancellation, of stable paths and of quasi-linear models are discussed in some detail.

Statistics and ProbabilityCombinatoricsStatement (computer science)Discrete mathematicsConditional independenceJoint probability distributionIndependence (mathematical logic)Matrix decompositionParametric statisticsCholesky decompositionMathematicsCorresponding conditionalBernoulli
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Binary distributions of concentric rings

2014

We introduce families of jointly symmetric, binary distributions that are generated over directed star graphs whose nodes represent variables and whose edges indicate positive dependences. The families are parametrized in terms of a single parameter. It is an outstanding feature of these distributions that joint probabilities relate to evenly spaced concentric rings. Kronecker product characterizations make them computationally attractive for a large number of variables. We study the behavior of different measures of dependence and derive maximum likelihood estimates when all nodes are observed and when the inner node is hidden.

Statistics and ProbabilityContingency tableKronecker productDiscrete mathematicsNumerical AnalysisBinary numberStar (graph theory)Combinatoricssymbols.namesakeConditional independenceJoint probability distributionsymbolsFeature (machine learning)Node (circuits)Statistics Probability and UncertaintyMathematicsJournal of Multivariate Analysis
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Centile estimation for a proportion response variable

2015

This paper introduces two general models for computing centiles when the response variable Y can take values between 0 and 1, inclusive of 0 or 1. The models developed are more flexible alternatives to the beta inflated distribution. The first proposed model employs a flexible four parameter logit skew Student t (logitSST) distribution to model the response variable Y on the unit interval (0, 1), excluding 0 and 1. This model is then extended to the inflated logitSST distribution for Y on the unit interval, including 1. The second model developed in this paper is a generalised Tobit model for Y on the unit interval, including 1. Applying these two models to (1-Y) rather than Y enables model…

Statistics and ProbabilityEstimationDistribution (number theory)EpidemiologyLogitSkew01 natural sciences010104 statistics & probability03 medical and health sciencesVariable (computer science)0302 clinical medicineUnit interval (data transmission)030225 pediatricsStatisticsProbability distributionTobit model0101 mathematicsMathematicsStatistics in Medicine
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Holt–Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data

2007

Abstract This paper provides a formulation for the additive Holt–Winters forecasting procedure that simplifies both obtaining maximum likelihood estimates of all unknowns, smoothing parameters and initial conditions, and the computation of point forecasts and reliable predictive intervals. The stochastic component of the model is introduced by means of additive, uncorrelated, homoscedastic and Normal errors, and then the joint distribution of the data vector, a multivariate Normal distribution, is obtained. In the case where a data transformation was used to improve the fit of the model, cumulative forecasts are obtained here using a Monte-Carlo approximation. This paper describes the metho…

Statistics and ProbabilityExponential smoothingData transformation (statistics)Prediction intervalMultivariate normal distributionJoint probability distributionHomoscedasticityStatisticsEconometricsStatistics Probability and UncertaintyTime seriesPhysics::Atmospheric and Oceanic PhysicsSmoothingMathematicsJournal of Applied Statistics
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Identifying Causal Effects with the R Package causaleffect

2017

Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-calculus, but the rules themselves do not give any direct indication whether the effect in question is identifiable or not. Shpitser and Pearl constructed an algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs. This algorithm can be seen as a repeated application of the rules of do-calculus and known properties of probabilities, and it ultimately eit…

Statistics and ProbabilityFOS: Computer and information sciencesTheoretical computer sciencecausalityDistribution (number theory)C-componentComputer sciencecausal model02 engineering and technologyCausal structureMethodology (stat.ME)03 medical and health sciences0302 clinical medicinedo-calculusJoint probability distribution0202 electrical engineering electronic engineering information engineering030212 general & internal medicineDAG; do-calculus; causality; causal model; identifiability; graph; C-component; hedge; d-separationlcsh:Statisticslcsh:HA1-4737Statistics - Methodologycomputer.programming_languageCausal modelta112DAGd-separationgraphhedgeidentifiabilityExpression (mathematics)PEARL (programming language)Action (philosophy)kausaliteetti020201 artificial intelligence & image processingStatistics Probability and UncertaintycomputerSoftware
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Global and multiple test procedures using ordered p-values—a review

2004

This paper reviews global and multiple tests for the combination ofn hypotheses using the orderedp-values of then individual tests. In 1987, Rohmel and Streitberg presented a general method to construct global level α tests based on orderedp-values when there exists no prior knowledge regarding the joint distribution of the corresponding test statistics. In the case of independent test statistics, construction of global tests is available by means of recursive formulae presented by Bicher (1989), Kornatz (1994) and Finner and Roters (1994). Multiple test procedures can be developed by applying the closed test principle using these global tests as building blocks. Liu (1996) proposed represe…

Statistics and ProbabilityGeneral methodTest proceduresJoint probability distributionExistential quantificationStatisticsApplied mathematicsStatistics Probability and UncertaintyConstruct (philosophy)Statistical hypothesis testingMathematicsDynamic testingTest (assessment)Statistical Papers
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Recursive estimation of the conditional geometric median in Hilbert spaces

2012

International audience; A recursive estimator of the conditional geometric median in Hilbert spaces is studied. It is based on a stochastic gradient algorithm whose aim is to minimize a weighted L1 criterion and is consequently well adapted for robust online estimation. The weights are controlled by a kernel function and an associated bandwidth. Almost sure convergence and L2 rates of convergence are proved under general conditions on the conditional distribution as well as the sequence of descent steps of the algorithm and the sequence of bandwidths. Asymptotic normality is also proved for the averaged version of the algorithm with an optimal rate of convergence. A simulation study confirm…

Statistics and ProbabilityMallows-Wasserstein distanceRobbins-Monroasymptotic normalityCLTcentral limit theoremAsymptotic distributionMathematics - Statistics TheoryStatistics Theory (math.ST)01 natural sciencesMallows–Wasserstein distanceonline data010104 statistics & probability[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]60F05FOS: MathematicsApplied mathematics[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]0101 mathematics62L20MathematicsaveragingSequential estimation010102 general mathematicsEstimatorRobbins–MonroConditional probability distribution[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]Geometric medianstochastic gradient[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]robust estimatorRate of convergenceConvergence of random variablesStochastic gradient.kernel regressionsequential estimationKernel regressionStatistics Probability and Uncertainty
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