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Karteikarten

Statistical Inference Term 1

Alex Carr Statistical Inference Year 2 Englisch 95 Karten

Statistical Inference Term 1

Karten in diesem Stapel (95)

  1. 1PDF of a random vectorPDF of a random vector
  2. 2CDF of a random vectorCDF of a random vector
  3. 3Marginal PDFMarginal PDF
  4. 4Conditional PDFConditional PDF
  5. 5IndependenceIndependence
  6. 6Random Sample, IIDRandom Sample, IID
  7. 7Expectation of a FunctionalExpectation of a Functional
  8. 8Variance MatrixVariance Matrix
  9. 9Proposition 2.1Proposition 2.1
  10. 10Proposition 2.2Proposition 2.2
  11. 11Theorem - Distribution of a Univariate TransformationTheorem - Distribution of a Univariate Transformation
  12. 12Theorem - Distribution of a Multivariate TransformationTheorem - Distribution of a Multivariate Transformation
  13. 13Multivariate Standard Normal DistributionMultivariate Standard Normal Distribution
  14. 14Linear Transformation of the MVNLinear Transformation of the MVN
  15. 15Proposition 2.7Proposition 2.7
  16. 16Data MatrixData Matrix
  17. 17Sample Mean VectorSample Mean Vector
  18. 18Sample Variance MatrixSample Variance Matrix
  19. 19Proposition 3.1Proposition 3.1
  20. 20Estimate and EstimatorEstimate and Estimator
  21. 21Bias and UnbiasedBias and Unbiased
  22. 22Standard ErrorStandard Error
  23. 23Mean-Square Error (MSE)Mean-Square Error (MSE)
  24. 24Theorem 3.1(a)Theorem 3.1(a)
  25. 25Theorem 3.1(b,c)Theorem 3.1(b,c)
  26. 26Theorem 3.2- Estimation of Means and VariancesTheorem 3.2- Estimation of Means and Variances
  27. 27Confidence IntervalConfidence Interval
  28. 28LemmaLemma
  29. 29Sampling Distribution for S^2Sampling Distribution for S^2
  30. 30Pivotal StatisticPivotal Statistic
  31. 31Confidence Interval for Sigma^2Confidence Interval for Sigma^2
  32. 32(Student's) t distribution(Student's) t distribution
  33. 33The t statisticThe t statistic
  34. 34Confidence Interval for mean when variance is unknownConfidence Interval for mean when variance is unknown
  35. 35Confidence Regions For the Multivariate Normal MeanConfidence Regions For the Multivariate Normal Mean
  36. 36LikelihoodLikelihood
  37. 37Maximum Likelihood EstimateMaximum Likelihood Estimate
  38. 38Likelihood EquationLikelihood Equation
  39. 39Multiparameter Likelihood EstimationMultiparameter Likelihood Estimation
  40. 40Proposition - Invariance of the MLEProposition - Invariance of the MLE
  41. 41Sufficient StatisticsSufficient Statistics
  42. 42Factorisation Theorem (Fisher-Neyman)Factorisation Theorem (Fisher-Neyman)
  43. 43Proposition 4.3Proposition 4.3
  44. 44Observed (fisher) informationObserved (fisher) information
  45. 45Expected (fisher) informationExpected (fisher) information
  46. 46Proposition 4.4 - Information for IID samplesProposition 4.4 - Information for IID samples
  47. 47Theorem 4.5 - Cramer RaoTheorem 4.5 - Cramer Rao
  48. 48EfficiencyEfficiency
  49. 49Lemma 2Lemma 2
  50. 50Regular LikelihoodRegular Likelihood
  51. 51Large-Sample distribution of theta hatLarge-Sample distribution of theta hat
  52. 52Proposition 4.7Proposition 4.7
  53. 53Wald IntervalsWald Intervals
  54. 54The Delta MethodThe Delta Method
  55. 55Likelihood Interval (Method of Support)Likelihood Interval (Method of Support)
  56. 56Profile LikelihoodProfile Likelihood
  57. 57Multiplication RuleMultiplication Rule
  58. 58Partition TheoremPartition Theorem
  59. 59Conditional IndependenceConditional Independence
  60. 60Bayes Theorem for Conditional Random VariablesBayes Theorem for Conditional Random Variables
  61. 61General Bayesian Statistical ProblemGeneral Bayesian Statistical Problem
  62. 62Conjugate Family, Conjugate PriorConjugate Family, Conjugate Prior
  63. 63Theorem 5.1 - The Beta Binomial ModelTheorem 5.1 - The Beta Binomial Model
  64. 64Properties of the Beta DistributionProperties of the Beta Distribution
  65. 65Core/Kernel of a DistributionCore/Kernel of a Distribution
  66. 66Lemma 3Lemma 3
  67. 67Theorem 5.2 - The Gamma Poisson ModelTheorem 5.2 - The Gamma Poisson Model
  68. 68Properties of The Gamma DistributionProperties of The Gamma Distribution
  69. 69PrecisionPrecision
  70. 70Conjugate Normal Sampling with Known VarianceConjugate Normal Sampling with Known Variance
  71. 71Conjugate Normal with Unknown VarianceConjugate Normal with Unknown Variance
  72. 72Proposition 5.5Proposition 5.5
  73. 73Vague PriorsVague Priors
  74. 74Improper PriorImproper Prior
  75. 75InvarianceInvariance
  76. 76Jeffreys PriorJeffreys Prior
  77. 77Credible Interval and Credible RegionCredible Interval and Credible Region
  78. 78Equal Tailed IntervalEqual Tailed Interval
  79. 79Highest Probability Density RegionHighest Probability Density Region
  80. 80Prior PredictivePrior Predictive
  81. 81Posterior PredictivePosterior Predictive
  82. 82Statistical Decision ProblemStatistical Decision Problem
  83. 83Risk, or risk functionRisk, or risk function
  84. 84Decision Rule or Decision FunctionDecision Rule or Decision Function
  85. 85Bayes RiskBayes Risk
  86. 86Bayes Decision or Bayes RuleBayes Decision or Bayes Rule
  87. 87Posterior Expected LossPosterior Expected Loss
  88. 88Proposition 6.1 : For given data x, the optimal decision d* minimizes for all x, the posterior expected loss:Proposition 6.1 : For given data x, the optimal decision d* minimizes for all x, the posterior expected loss:
  89. 89Quadratic loss, squared-error lossQuadratic loss, squared-error loss
  90. 90Proposition 6.2 - Bayesian estimation under quadratic lossProposition 6.2 - Bayesian estimation under quadratic loss
  91. 91Absolute-error lossAbsolute-error loss
  92. 92Proposition 6.3 - Bayesian estimate under absolute error lossProposition 6.3 - Bayesian estimate under absolute error loss
  93. 93Zero-one lossZero-one loss
  94. 94Proposition 6.4 - Bayesian estimate under zero one lossProposition 6.4 - Bayesian estimate under zero one loss
  95. 95Theorem 6.5 - Bernstein -von MisesTheorem 6.5 - Bernstein -von Mises

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Year 2 Statistical Inference Karteikarten am Statistical Inference Term 1 , erstellt von Alex Carr am 27/11/2022.
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Alex Carr
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