The Annals of Statistics, ISSN 0090-5364, 6/2009, Volume 37, Issue 3, pp. 1207 - 1228

It has long been known that for the comparison of pairwise nested models, a decision based on the Bayes factor produces a consistent model selector (in the...

Mathematical procedures | Approximation | Sample size | Bayesian networks | Induced substructures | Linear models | Parametric models | Paradoxes | Frequentism | Probabilities | Bayes factors | Consistency | Intrinsic priors | intrinsic priors | MODEL SELECTION | STATISTICS & PROBABILITY | HYPOTHESES | linear models | consistency | 62J15 | 62F05

Mathematical procedures | Approximation | Sample size | Bayesian networks | Induced substructures | Linear models | Parametric models | Paradoxes | Frequentism | Probabilities | Bayes factors | Consistency | Intrinsic priors | intrinsic priors | MODEL SELECTION | STATISTICS & PROBABILITY | HYPOTHESES | linear models | consistency | 62J15 | 62F05

Journal Article

The Annals of statistics, ISSN 0090-5364, 6/2015, Volume 43, Issue 3, pp. 1243 - 1272

This paper is concerned with the problems of interaction screening and nonlinear classification in a high-dimensional setting. We propose a two-step procedure,...

PRECISION MATRIX ESTIMATION | discriminant analysis | DANTZIG SELECTOR | ENVIRONMENT INTERACTIONS | LOGISTIC-REGRESSION | LINEAR DISCRIMINANT-ANALYSIS | STATISTICS & PROBABILITY | sparsity | dimension reduction | VARIABLE SELECTION | interaction screening | MODELS | sure screening property | Classification | LASSO | GENE-EXPRESSION | COVARIANCE ESTIMATION | Statistics - Machine Learning | 62H30 | 62F05 | 62J12

PRECISION MATRIX ESTIMATION | discriminant analysis | DANTZIG SELECTOR | ENVIRONMENT INTERACTIONS | LOGISTIC-REGRESSION | LINEAR DISCRIMINANT-ANALYSIS | STATISTICS & PROBABILITY | sparsity | dimension reduction | VARIABLE SELECTION | interaction screening | MODELS | sure screening property | Classification | LASSO | GENE-EXPRESSION | COVARIANCE ESTIMATION | Statistics - Machine Learning | 62H30 | 62F05 | 62J12

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2009, Volume 37, Issue 5A, pp. 2523 - 2542

Normal mixture distributions are arguably the most important mixture models, and also the most technically challenging. The likelihood function of the normal...

Statistical variance | Penalty function | Null hypothesis | Statistical discrepancies | Critical values | Schizophrenia | Ratio test | P values | Parametric models | Statistics | Statistical genetics | Normal mixture models | Compactness | Likelihood ratio test | Homogeneity test | Chi-square limiting distribution | POPULATION | HOMOGENEITY | NUMBER | compactness | likelihood ratio test | STRUCTURAL PARAMETER | COMPONENTS | statistical genetics | STATISTICS & PROBABILITY | homogeneity test | VARIABLE SELECTION | normal mixture models | GENE | ASYMPTOTICS | 62F03 | 62F05

Statistical variance | Penalty function | Null hypothesis | Statistical discrepancies | Critical values | Schizophrenia | Ratio test | P values | Parametric models | Statistics | Statistical genetics | Normal mixture models | Compactness | Likelihood ratio test | Homogeneity test | Chi-square limiting distribution | POPULATION | HOMOGENEITY | NUMBER | compactness | likelihood ratio test | STRUCTURAL PARAMETER | COMPONENTS | statistical genetics | STATISTICS & PROBABILITY | homogeneity test | VARIABLE SELECTION | normal mixture models | GENE | ASYMPTOTICS | 62F03 | 62F05

Journal Article

Annals of Statistics, ISSN 0090-5364, 06/2018, Volume 46, Issue 3, pp. 1352 - 1382

This paper studies hypothesis testing and parameter estimation in the context of the divide-and-conquer algorithm. In a unified likelihood-based framework, we...

Debiasing | Thresholding | Divide and conquer | Massive data | REGRESSION | thresholding | CONFIDENCE-INTERVALS | debiasing | STATISTICS & PROBABILITY | massive data | VARIABLE SELECTION | LINEAR-MODELS | NP-DIMENSIONALITY | NONCONCAVE PENALIZED LIKELIHOOD | RATES | GENERAL-THEORY | REGIONS | LASSO | Primary 62F05 | secondary 62F12 | 62F10

Debiasing | Thresholding | Divide and conquer | Massive data | REGRESSION | thresholding | CONFIDENCE-INTERVALS | debiasing | STATISTICS & PROBABILITY | massive data | VARIABLE SELECTION | LINEAR-MODELS | NP-DIMENSIONALITY | NONCONCAVE PENALIZED LIKELIHOOD | RATES | GENERAL-THEORY | REGIONS | LASSO | Primary 62F05 | secondary 62F12 | 62F10

Journal Article

5.
Full Text
Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models

Annales de l'institut Henri Poincare (B) Probability and Statistics, ISSN 0246-0203, 12/2008, Volume 44, Issue 6, pp. 1096 - 1127

In testing that a given distribution P belongs to a parameterized family P. one is often led to compare a nonparametric estimate A(n) of some functional A of P...

Semiparametric estimation | Parametric bootstrap | Copula | P-values | Goodness-of-fit test | Monte Carlo simulation | WEAK-CONVERGENCE | STATISTICS & PROBABILITY | INFERENCE | COPULA MODELS | 62H15 | 62F40 | 62F05

Semiparametric estimation | Parametric bootstrap | Copula | P-values | Goodness-of-fit test | Monte Carlo simulation | WEAK-CONVERGENCE | STATISTICS & PROBABILITY | INFERENCE | COPULA MODELS | 62H15 | 62F40 | 62F05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 4/2009, Volume 37, Issue 2, pp. 596 - 618

In this paper we introduce and investigate a new rejection curve for asymptotic control of the false discovery rate (FDR) in multiple hypotheses testing...

Mathematical procedures | Error rates | Null hypothesis | Logical proofs | Threshing | Heuristics | Critical values | Applied statistics | P values | Diabetes | Crossing point | Multiple test procedure | False discovery proportion | Positive regression dependent | Multiple comparisons | Extended glivenko-cantelli theorem | False discovery rate | Step-up-down test | Least favorable configurations | Familywise error rate | Order statistics | Step-up test | multiple test procedure | order statistics | NUMBER | positive regression dependent | step-up test | STATISTICS & PROBABILITY | least favorable configurations | HYPOTHESES | extended Glivenko-Cantelli theorem | false discovery proportion | familywise error rate | false discovery rate | multiple comparisons | step-up-down test | 62J15 | 60F99 | extended Glivenko–Cantelli theorem | 62F03 | 62F05

Mathematical procedures | Error rates | Null hypothesis | Logical proofs | Threshing | Heuristics | Critical values | Applied statistics | P values | Diabetes | Crossing point | Multiple test procedure | False discovery proportion | Positive regression dependent | Multiple comparisons | Extended glivenko-cantelli theorem | False discovery rate | Step-up-down test | Least favorable configurations | Familywise error rate | Order statistics | Step-up test | multiple test procedure | order statistics | NUMBER | positive regression dependent | step-up test | STATISTICS & PROBABILITY | least favorable configurations | HYPOTHESES | extended Glivenko-Cantelli theorem | false discovery proportion | familywise error rate | false discovery rate | multiple comparisons | step-up-down test | 62J15 | 60F99 | extended Glivenko–Cantelli theorem | 62F03 | 62F05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 8/2007, Volume 35, Issue 4, pp. 1535 - 1558

Kernel methods for deconvolution have attractive features, and prevail in the literature. However, they have disadvantages, which include the fact that they...

Integers | Density estimation | Error rates | Integrands | Nonparametric Curve Estimation and Deconvolution | Eigenfunctions | Fourier transformations | Real lines | Density | Estimators | Earthing up | Kernel methods | Optimal convergence rates | Smoothing-parameter choice | Errors in variables | Cross-validation | Density deconvolution | Minimax optimality | REGRESSION | errors in variables | CONTAMINATED SAMPLE | optimal convergence rates | STATISTICS & PROBABILITY | cross-validation | BANDWIDTH SELECTION | density deconvolution | KERNEL DENSITY-ESTIMATION | DISTRIBUTIONS | minimax optimality | OPTIMAL RATES | MEASUREMENT ERROR | kernel methods | CONVERGENCE | VARIABLES | smoothing-parameter choice | 62G07 | 62F05

Integers | Density estimation | Error rates | Integrands | Nonparametric Curve Estimation and Deconvolution | Eigenfunctions | Fourier transformations | Real lines | Density | Estimators | Earthing up | Kernel methods | Optimal convergence rates | Smoothing-parameter choice | Errors in variables | Cross-validation | Density deconvolution | Minimax optimality | REGRESSION | errors in variables | CONTAMINATED SAMPLE | optimal convergence rates | STATISTICS & PROBABILITY | cross-validation | BANDWIDTH SELECTION | density deconvolution | KERNEL DENSITY-ESTIMATION | DISTRIBUTIONS | minimax optimality | OPTIMAL RATES | MEASUREMENT ERROR | kernel methods | CONVERGENCE | VARIABLES | smoothing-parameter choice | 62G07 | 62F05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2010, Volume 38, Issue 5, pp. 3063 - 3092

We study a class of hypothesis testing problems in which, upon observing the realization of an n-dimensional Gaussian vector, one has to decide whether the...

Integers | Hypothesis testing | Null hypothesis | Algorithms | Mathematical sets | Critical values | Polynomials | Mathematical vectors | Random variables | Multiple hypotheses | Gaussian processes | STATISTICS & PROBABILITY | multiple hypotheses | UNIFORM | HIGH DIMENSION | 62F03 | 62F05

Integers | Hypothesis testing | Null hypothesis | Algorithms | Mathematical sets | Critical values | Polynomials | Mathematical vectors | Random variables | Multiple hypotheses | Gaussian processes | STATISTICS & PROBABILITY | multiple hypotheses | UNIFORM | HIGH DIMENSION | 62F03 | 62F05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 4/2006, Volume 34, Issue 2, pp. 837 - 877

We consider the asymptotic behavior of posterior distributions if the model is misspecified. Given a prior distribution and a random sample from a distribution...

Minimax | Topological theorems | Bayesian Analysis | Average linear density | Entropy | Mathematical functions | Convexity | Logarithms | Density | Parametric models | Perceptron convergence procedure | Infinite-dimensional model | Rate of convergence | Misspecification | Posterior distribution | MAXIMUM-LIKELIHOOD | infinite-dimensional model | SPACES | BEHAVIOR | rate of convergence | misspecification | STATISTICS & PROBABILITY | MIXTURES | CONSISTENCY | posterior distribution | INCORRECT | POSTERIOR DISTRIBUTIONS | CONVERGENCE-RATES | 62G07 | 62G08 | 62G20 | 62F15 | 62F05

Minimax | Topological theorems | Bayesian Analysis | Average linear density | Entropy | Mathematical functions | Convexity | Logarithms | Density | Parametric models | Perceptron convergence procedure | Infinite-dimensional model | Rate of convergence | Misspecification | Posterior distribution | MAXIMUM-LIKELIHOOD | infinite-dimensional model | SPACES | BEHAVIOR | rate of convergence | misspecification | STATISTICS & PROBABILITY | MIXTURES | CONSISTENCY | posterior distribution | INCORRECT | POSTERIOR DISTRIBUTIONS | CONVERGENCE-RATES | 62G07 | 62G08 | 62G20 | 62F15 | 62F05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 8/2013, Volume 41, Issue 4, pp. 1716 - 1741

Uniformly most powerful tests are statistical hypothesis tests that provide the greatest power against a fixed null hypothesis among all tests of a given size....

Null hypothesis | Sample size | Logical givens | Weight of evidence | P values | Binomials | Statistics | Parametric models | Density | Probabilities | Higgs boson | One-parameter exponential family model | Jeffreys-Lindley paradox | Neyman-Pearson lemma | Objective Bayes | Uniformly most powerful test | Bayes factor | Nonlocal prior density | one-parameter exponential family model | STATISTICAL-INFERENCE | nonlocal prior density | MODEL SELECTION | objective Bayes | HYPOTHESIS | STATISTICS & PROBABILITY | uniformly most powerful test | Jeffreys–Lindley paradox | 62A01 | Neyman–Pearson lemma | 62F03 | 62F15 | 62F05

Null hypothesis | Sample size | Logical givens | Weight of evidence | P values | Binomials | Statistics | Parametric models | Density | Probabilities | Higgs boson | One-parameter exponential family model | Jeffreys-Lindley paradox | Neyman-Pearson lemma | Objective Bayes | Uniformly most powerful test | Bayes factor | Nonlocal prior density | one-parameter exponential family model | STATISTICAL-INFERENCE | nonlocal prior density | MODEL SELECTION | objective Bayes | HYPOTHESIS | STATISTICS & PROBABILITY | uniformly most powerful test | Jeffreys–Lindley paradox | 62A01 | Neyman–Pearson lemma | 62F03 | 62F15 | 62F05

Journal Article

Extremes, ISSN 1386-1999, 12/2016, Volume 19, Issue 4, pp. 627 - 660

Spatially isotropic max-stable processes have been used to model extreme spatial or space-time observations. One prominent model is the Brown-Resnick process,...

62P12 | Brown-Resnick space-time process | Civil Engineering | Max-stable process | Statistics, general | Statistics | Pairwise maximum likelihood estimate | Primary–62G32 | Max-stable model check | Secondary–62F05 | Hypothesis test for spatial isotropy | Hydrogeology | Statistics for Business/Economics/Mathematical Finance/Insurance | Quality Control, Reliability, Safety and Risk | Anisotropic space-time process | Pairwise likelihood | 62F12 | 62M40 | Environmental Management | MIXING RANDOM-FIELDS | STATISTICS & PROBABILITY | INFERENCE | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | EXTREMES | MAX-STABLE PROCESSES | Anisotropy | Geospatial data | Precipitation (Meteorology) | Studies | Statistical analysis | Risk assessment | Spacetime | Asymptotic properties | Time series | Consistency | Mathematical models | Conditional probability | Assessments | Statistical tests | Statistics - Methodology

62P12 | Brown-Resnick space-time process | Civil Engineering | Max-stable process | Statistics, general | Statistics | Pairwise maximum likelihood estimate | Primary–62G32 | Max-stable model check | Secondary–62F05 | Hypothesis test for spatial isotropy | Hydrogeology | Statistics for Business/Economics/Mathematical Finance/Insurance | Quality Control, Reliability, Safety and Risk | Anisotropic space-time process | Pairwise likelihood | 62F12 | 62M40 | Environmental Management | MIXING RANDOM-FIELDS | STATISTICS & PROBABILITY | INFERENCE | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | EXTREMES | MAX-STABLE PROCESSES | Anisotropy | Geospatial data | Precipitation (Meteorology) | Studies | Statistical analysis | Risk assessment | Spacetime | Asymptotic properties | Time series | Consistency | Mathematical models | Conditional probability | Assessments | Statistical tests | Statistics - Methodology

Journal Article

Journal of Mathematical Biology, ISSN 0303-6812, 7/2019, Volume 79, Issue 2, pp. 485 - 508

The transfer distance (TD) was introduced in the classification framework and studied in the context of phylogenetic tree matching. Recently, Lemoine et al....

60E15 | Phylogenetic trees | 62P10 | Mathematical and Computational Biology | Primary 92-08 | Lattice paths | Mathematics | R-distance | Concentration inequalities | Random phylogenies | 62F40 | Applications of Mathematics | 62F35 | Secondary 05A05 | 05C05 | Distances between bipartitions and phylogenies | 62F05 | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | CONFIDENCE | Research | Biomathematics | Mathematical research | Bootstrapping (Statistics) | Trees | Taxa | Computer simulation | Asymptotic properties | Phylogenetics | Mathematical models | Phylogeny | Convergence | Life Sciences | Biodiversity | Systematics, Phylogenetics and taxonomy

60E15 | Phylogenetic trees | 62P10 | Mathematical and Computational Biology | Primary 92-08 | Lattice paths | Mathematics | R-distance | Concentration inequalities | Random phylogenies | 62F40 | Applications of Mathematics | 62F35 | Secondary 05A05 | 05C05 | Distances between bipartitions and phylogenies | 62F05 | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | CONFIDENCE | Research | Biomathematics | Mathematical research | Bootstrapping (Statistics) | Trees | Taxa | Computer simulation | Asymptotic properties | Phylogenetics | Mathematical models | Phylogeny | Convergence | Life Sciences | Biodiversity | Systematics, Phylogenetics and taxonomy

Journal Article

13.
Full Text
Large-Scale Multi-Stream Quickest Change Detection via Shrinkage Post-Change Estimation

IEEE Transactions on Information Theory, ISSN 0018-9448, 12/2015, Volume 61, Issue 12, pp. 6926 - 6938

The quickest change detection problem is considered in the context of monitoring large-scale independent normal distributed data streams with possible changes...

Context | quickest detection | Maximum likelihood estimation | sequential detection | Distributed databases | Asymptotic optimality | Delays | shrinkage estimation | Monitoring | Method of moments | change-point | Shiryaev-Roberts | COMPUTER SCIENCE, INFORMATION SYSTEMS | ENGINEERING, ELECTRICAL & ELECTRONIC | Monte Carlo method | Usage | Electronic data processing | Research | Maximum likelihood estimates (Statistics)

Context | quickest detection | Maximum likelihood estimation | sequential detection | Distributed databases | Asymptotic optimality | Delays | shrinkage estimation | Monitoring | Method of moments | change-point | Shiryaev-Roberts | COMPUTER SCIENCE, INFORMATION SYSTEMS | ENGINEERING, ELECTRICAL & ELECTRONIC | Monte Carlo method | Usage | Electronic data processing | Research | Maximum likelihood estimates (Statistics)

Journal Article

The Annals of Statistics, ISSN 0090-5364, 8/2010, Volume 38, Issue 4, pp. 1937 - 1952

In the class of normal regression models with a finite number of regressors, and for a wide class of prior distributions, a Bayesian model selection procedure...

Approximation | Integrands | Sample size | Infinity | Regression analysis | Bayesian networks | Parametric models | Linear models | Modeling | Paradoxes | Rate of growth | Multiplicity of parameters | Bayes factors | Bic | Intrinsic priors | intrinsic priors | STATISTICS & PROBABILITY | rate of growth | multiplicity of parameters | BIC | linear models | VARIABLE SELECTION | 62J15 | 62F05

Approximation | Integrands | Sample size | Infinity | Regression analysis | Bayesian networks | Parametric models | Linear models | Modeling | Paradoxes | Rate of growth | Multiplicity of parameters | Bayes factors | Bic | Intrinsic priors | intrinsic priors | STATISTICS & PROBABILITY | rate of growth | multiplicity of parameters | BIC | linear models | VARIABLE SELECTION | 62J15 | 62F05

Journal Article

The Annals of Applied Probability, ISSN 1050-5164, 12/2008, Volume 18, Issue 6, pp. 2337 - 2366

Let ${\bf X}_{1},\ldots,{\bf X}_{n}$ be a random sample from a p-dimensional population distribution. Assume that $c_{1}n^{\alpha}\leq p\leq c_{2}n^{\alpha}$...

Approximation | Random sampling | Statistical theories | Eigenvalues | Population distributions | Random variables | Significance level | Sampling distributions | Covariance matrices | Independence test | Stochastic optimization | Berry-Esseen bound | Correlation matrices | Extreme distribution | extreme distribution | UNCERTAINTY PRINCIPLES | correlation matrices | STATISTICS & PROBABILITY | stochastic optimization | Mathematics - Probability | Berry–Esseen bound | 60F05 | 62F05

Approximation | Random sampling | Statistical theories | Eigenvalues | Population distributions | Random variables | Significance level | Sampling distributions | Covariance matrices | Independence test | Stochastic optimization | Berry-Esseen bound | Correlation matrices | Extreme distribution | extreme distribution | UNCERTAINTY PRINCIPLES | correlation matrices | STATISTICS & PROBABILITY | stochastic optimization | Mathematics - Probability | Berry–Esseen bound | 60F05 | 62F05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 8/2007, Volume 35, Issue 4, pp. 1432 - 1455

Some effort has been undertaken over the last decade to provide conditions for the control of the false discovery rate by the linear step-up procedure (LSU)...

Error rates | Degrees of freedom | Null hypothesis | Infinity | Statistical theories | Applied statistics | P values | Random variables | Multiple Testing and High-Dimensional Data | Statistics | T distribution | Largest crossing point | Multiple test procedure | Multiple comparisons | False discovery rate | Multivariate total positivity of order 2 | Glivenko-Cantelli theorem | Least favorable configurations | Exchangeable test statistics | Expected error rate | multiple test procedure | Simes' test | INEQUALITIES | I ERRORS | multivariate total positivity of order 2 | STATISTICS & PROBABILITY | expected error rate | least favorable configurations | exchangeable test statistics | positive regression dependency | largest crossing point | false discovery rate | multiple comparisons | 62J15 | 60F99 | Glivenko–Cantelli theorem | Simes’ test | 62F03 | 62F05

Error rates | Degrees of freedom | Null hypothesis | Infinity | Statistical theories | Applied statistics | P values | Random variables | Multiple Testing and High-Dimensional Data | Statistics | T distribution | Largest crossing point | Multiple test procedure | Multiple comparisons | False discovery rate | Multivariate total positivity of order 2 | Glivenko-Cantelli theorem | Least favorable configurations | Exchangeable test statistics | Expected error rate | multiple test procedure | Simes' test | INEQUALITIES | I ERRORS | multivariate total positivity of order 2 | STATISTICS & PROBABILITY | expected error rate | least favorable configurations | exchangeable test statistics | positive regression dependency | largest crossing point | false discovery rate | multiple comparisons | 62J15 | 60F99 | Glivenko–Cantelli theorem | Simes’ test | 62F03 | 62F05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 6/2011, Volume 39, Issue 3, pp. 1551 - 1579

Within a Bayesian decision theoretic framework we investigate some asymptotic optimality properties of a large class of multiple testing rules. A parametric...

Mathematical procedures | Null hypothesis | Approximation | Infinity | Threshing | False negative errors | Blood grouping | P values | Oracles | Bayes theorem | Asymptotic optimality | Bayes oracle | Multiple testing | FDR | FALSE DISCOVERY RATE | EMPIRICAL-BAYES | RATES | NUMBER | NULL | STATISTICS & PROBABILITY | asymptotic optimality | HYPOTHESES | PROPORTION | 62C10 | 62C25 | 62F05

Mathematical procedures | Null hypothesis | Approximation | Infinity | Threshing | False negative errors | Blood grouping | P values | Oracles | Bayes theorem | Asymptotic optimality | Bayes oracle | Multiple testing | FDR | FALSE DISCOVERY RATE | EMPIRICAL-BAYES | RATES | NUMBER | NULL | STATISTICS & PROBABILITY | asymptotic optimality | HYPOTHESES | PROPORTION | 62C10 | 62C25 | 62F05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 2/2006, Volume 34, Issue 1, pp. 92 - 122

In the sequential change-point detection literature, most research specifies a required frequency of false alarms at a given pre-change distribution and tries...

Hypothesis testing | Mathematical procedures | Null hypothesis | Surveillance | False alarms | Geologic eons | Mathematics | Asymptotic theory | Stopping distances | Continuous functions | Parametric Inference | Statistical process control | Power one tests | Optimizer | Quality control | Asymptotic optimality | Change-point | REGRESSION | TESTS | quality control | STOPPING RULES | STATISTICS & PROBABILITY | asymptotic optimality | AVERAGE RUN-LENGTH | statistical process control | power one tests | optimizer | FALSE ALARM | SYSTEMS | surveillance | change-point | OPTIMALITY | 62L15 | 62L10 | 62F05

Hypothesis testing | Mathematical procedures | Null hypothesis | Surveillance | False alarms | Geologic eons | Mathematics | Asymptotic theory | Stopping distances | Continuous functions | Parametric Inference | Statistical process control | Power one tests | Optimizer | Quality control | Asymptotic optimality | Change-point | REGRESSION | TESTS | quality control | STOPPING RULES | STATISTICS & PROBABILITY | asymptotic optimality | AVERAGE RUN-LENGTH | statistical process control | power one tests | optimizer | FALSE ALARM | SYSTEMS | surveillance | change-point | OPTIMALITY | 62L15 | 62L10 | 62F05

Journal Article

Journal of Statistical Theory and Practice, ISSN 1559-8608, 6/2019, Volume 13, Issue 2, pp. 1 - 28

Also in a time series context, the simple Poisson distribution is very popular for modeling the marginal distribution of the generated counts. If, in contrast,...

Hypothesis test | 62M10 | Bias correction | Poisson INARMA process | Bivariate dispersion index | Statistical Theory and Methods | Probability Theory and Stochastic Processes | Statistics, general | Bivariate Poisson distribution | Statistics | 62F03 | 62F05

Hypothesis test | 62M10 | Bias correction | Poisson INARMA process | Bivariate dispersion index | Statistical Theory and Methods | Probability Theory and Stochastic Processes | Statistics, general | Bivariate Poisson distribution | Statistics | 62F03 | 62F05

Journal Article

The Annals of Statistics, ISSN 0090-5364, 8/2006, Volume 34, Issue 4, pp. 2015 - 2025

Linear regression models are among the models most used in practice, although the practitioners are often not sure whether their assumed linear regression...

Design and Regression Analysis | Statistical models | Linear regression | Inference | Experiment design | Polynomials | Design efficiency | Regression analysis | Parametric models | Degrees of polynomials | Polynomial regression of degree k - 1 | Testing lack of fit | Linear regression models | Efficient maximin power | Optimal designs to estimate the highest coefficient | LINEAR-REGRESSION MODELS | DISCRIMINATION | STATISTICS & PROBABILITY | linear regression models | optimal designs to estimate the highest coefficient | efficient maximin power | polynomial regression of degree k-1 | testing lack of fit | 62J05 | polynomial regression of degree k−1 | 62F05

Design and Regression Analysis | Statistical models | Linear regression | Inference | Experiment design | Polynomials | Design efficiency | Regression analysis | Parametric models | Degrees of polynomials | Polynomial regression of degree k - 1 | Testing lack of fit | Linear regression models | Efficient maximin power | Optimal designs to estimate the highest coefficient | LINEAR-REGRESSION MODELS | DISCRIMINATION | STATISTICS & PROBABILITY | linear regression models | optimal designs to estimate the highest coefficient | efficient maximin power | polynomial regression of degree k-1 | testing lack of fit | 62J05 | polynomial regression of degree k−1 | 62F05

Journal Article

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