The Annals of Statistics, ISSN 0090-5364, 10/2006, Volume 34, Issue 5, pp. 2367 - 2386

Let be a random vector. It is desired to predict Y based on . Examples of prediction methods are regression, classification using logistic regression or...

Mathematical procedures | Penalty function | Lipschitz condition | Statistical Learning | Sample size | Linear regression | Cubes | Inference | Mathematical independent variables | Entropy | Estimators | Persistence | Variable selection | persistence | 62C99

Mathematical procedures | Penalty function | Lipschitz condition | Statistical Learning | Sample size | Linear regression | Cubes | Inference | Mathematical independent variables | Entropy | Estimators | Persistence | Variable selection | persistence | 62C99

Journal Article

Tatra Mountains Mathematical Publications, ISSN 1210-3195, 12/2018, Volume 72, Issue 1, pp. 43 - 54

In this paper, we analyse properties of aggregation-based extensions of fuzzy measures depending on properties of aggregation functions which they are based...

aggregation function | Möbius transform | fuzzy measure | 28E10 | 62C99

aggregation function | Möbius transform | fuzzy measure | 28E10 | 62C99

Journal Article

Communications in Statistics - Simulation and Computation, ISSN 0361-0918, 08/2019, Volume 48, Issue 7, pp. 1922 - 1947

We propose an extension of parametric product partition models. We name our proposal nonparametric product partition models because we associate a random...

Loss functions | Bayesian nonparametric inference | 62G99 | Missing values | 62F15 | 62C99 | STATISTICS & PROBABILITY | PROBABILITY | BAYESIAN IDENTIFICATION | Skewed distributions | Missing data | Partitions | Change detection | Distribution functions

Loss functions | Bayesian nonparametric inference | 62G99 | Missing values | 62F15 | 62C99 | STATISTICS & PROBABILITY | PROBABILITY | BAYESIAN IDENTIFICATION | Skewed distributions | Missing data | Partitions | Change detection | Distribution functions

Journal Article

Communications in Statistics - Theory and Methods, ISSN 0361-0926, 09/2019, Volume 48, Issue 17, pp. 4320 - 4338

In this paper, assuming that the error terms follow a multivariate t distribution, we derive the exact formula for the predictive mean squared error (PMSE) of...

multivariate t error term | Predictive mean squared error | heterogeneous preliminary test estimator | 62J07 | homogeneous preliminary test estimator | 62C99 | LINEAR-REGRESSION MODEL | STATISTICS & PROBABILITY | RESTRICTIONS | MSE PERFORMANCE | Errors | Economic models | Multivariate analysis | Estimators

multivariate t error term | Predictive mean squared error | heterogeneous preliminary test estimator | 62J07 | homogeneous preliminary test estimator | 62C99 | LINEAR-REGRESSION MODEL | STATISTICS & PROBABILITY | RESTRICTIONS | MSE PERFORMANCE | Errors | Economic models | Multivariate analysis | Estimators

Journal Article

Communications in Statistics - Theory and Methods, ISSN 0361-0926, 06/2019, Volume 48, Issue 11, pp. 2748 - 2765

Improved point and interval estimation of the smallest scale parameter of n independent populations following two-parameter exponential distributions are...

Maruyama-type estimators | Stein-type estimators | Decision theory | 62C99 | 62F30 | 62F99 | Strawderman-type estimators | Kubokawa's methodology | Brewster and Zidek-type estimators | Kubokawa’s methodology | STATISTICS & PROBABILITY | INADMISSIBILITY | VARIANCE | Sampling | Parameter estimation | Estimators

Maruyama-type estimators | Stein-type estimators | Decision theory | 62C99 | 62F30 | 62F99 | Strawderman-type estimators | Kubokawa's methodology | Brewster and Zidek-type estimators | Kubokawa’s methodology | STATISTICS & PROBABILITY | INADMISSIBILITY | VARIANCE | Sampling | Parameter estimation | Estimators

Journal Article

The Annals of Statistics, ISSN 0090-5364, 2/2012, Volume 40, Issue 1, pp. 561 - 592

We investigate proper scoring rules for continuous distributions on the real line. It is known that the log score is the only such rule that depends on the...

Geometry | Differential operators | Integrands | Integration by parts | Entropy | Real lines | Unbiased estimators | Calculus of variations | Density | Concavity | Bregman score | Divergence | Variational methods | Local function | Score matching | Homogeneity | Euler-Lagrange equation | variational methods | divergence | entropy | local function | STATISTICS & PROBABILITY | concavity | integration by parts | INFERENCE | score matching | homogeneity | Euler–Lagrange equation | 62A99 | 62C99

Geometry | Differential operators | Integrands | Integration by parts | Entropy | Real lines | Unbiased estimators | Calculus of variations | Density | Concavity | Bregman score | Divergence | Variational methods | Local function | Score matching | Homogeneity | Euler-Lagrange equation | variational methods | divergence | entropy | local function | STATISTICS & PROBABILITY | concavity | integration by parts | INFERENCE | score matching | homogeneity | Euler–Lagrange equation | 62A99 | 62C99

Journal Article

Communications in Statistics - Simulation and Computation, ISSN 0361-0918, 11/2017, Volume 46, Issue 10, pp. 8233 - 8250

Traditional phase III clinical trials are powered to detect an overall treatment effect. However, it has increasingly been shown that many treatments are...

Adaptive signature design | interaction | classification | machine learning | dimension reduction | 62C99 | DECISION | LASSO | STATISTICS & PROBABILITY | reduction | SELECTION | dimension | Clinical trials | Medical research | Mathematical models | Computer simulation

Adaptive signature design | interaction | classification | machine learning | dimension reduction | 62C99 | DECISION | LASSO | STATISTICS & PROBABILITY | reduction | SELECTION | dimension | Clinical trials | Medical research | Mathematical models | Computer simulation

Journal Article

Statistics, ISSN 0233-1888, 01/2018, Volume 52, Issue 1, pp. 99 - 114

For an arbitrary strictly convex loss function, we study the problem of estimating a linear parametric function is a known constant, when a doubly censored...

censored samples | 62F10 | inadmissibility | equivariant estimator | 62C99 | Brewster-Zidek estimator | LOCATION | INTERVAL | SCALE PARAMETER | STATISTICS & PROBABILITY | PERCENTILE | VARIANCE | RISK EQUIVARIANT ESTIMATOR

censored samples | 62F10 | inadmissibility | equivariant estimator | 62C99 | Brewster-Zidek estimator | LOCATION | INTERVAL | SCALE PARAMETER | STATISTICS & PROBABILITY | PERCENTILE | VARIANCE | RISK EQUIVARIANT ESTIMATOR

Journal Article

Scientometrics, ISSN 0138-9130, 05/2017, Volume 111, Issue 2, pp. 581 - 593

Rating scales are used to elicit data about qualitative entities (e.g., research collaboration). This study presents an innovative method for reducing the...

Reduction | Receiver operator characteristic | Prediction | Rating scale | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MODELS | PAIRWISE COMPARISONS METHOD | INFORMATION SCIENCE & LIBRARY SCIENCE | Data collection | Discriminant analysis | Qualitative analysis | Factor analysis | Statistical analysis | 62P10 | 62C99 | 94A50 | 62C25

Reduction | Receiver operator characteristic | Prediction | Rating scale | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MODELS | PAIRWISE COMPARISONS METHOD | INFORMATION SCIENCE & LIBRARY SCIENCE | Data collection | Discriminant analysis | Qualitative analysis | Factor analysis | Statistical analysis | 62P10 | 62C99 | 94A50 | 62C25

Journal Article

The Annals of Statistics, ISSN 0090-5364, 2/2012, Volume 40, Issue 1, pp. 593 - 608

A scoring rule is a loss function measuring the quality of a quoted probability distribution Q for a random variable X, in the light of the realized outcome x...

Maximum likelihood estimation | Random sampling | Neighborhood conditions | Probability forecasts | Entropy | Mathematical functions | Unbiased estimators | Sampling distributions | Freshwater fishes | Estimators | Homogeneous function | Euler's theorem | Concavity | Supergradient | entropy | homogeneous function | INFORMATION | STATISTICS & PROBABILITY | supergradient | 62A99 | 62C99 | Euler’s theorem

Maximum likelihood estimation | Random sampling | Neighborhood conditions | Probability forecasts | Entropy | Mathematical functions | Unbiased estimators | Sampling distributions | Freshwater fishes | Estimators | Homogeneous function | Euler's theorem | Concavity | Supergradient | entropy | homogeneous function | INFORMATION | STATISTICS & PROBABILITY | supergradient | 62A99 | 62C99 | Euler’s theorem

Journal Article

Journal of Mathematical Chemistry, ISSN 0259-9791, 8/2019, Volume 57, Issue 7, pp. 1755 - 1769

We analyze the problem of selecting the model that best describes a given dataset. We focus on the case where the best model is the one with the smallest...

Error assessment | 62P99 | Model selection | Consistency | 62-07 | Theoretical and Computational Chemistry | Chemistry | Physical Chemistry | Comparisons | 62C99 | 26B05 | 26D07 | Math. Applications in Chemistry | MOLECULAR QUANTUM SIMILARITY | CRITERIA | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MATHEMATICAL-MODEL | VALIDATION | ERROR MEASURE | CHEMISTRY, MULTIDISCIPLINARY | Evaluation | Models | Statistical models | Statistics

Error assessment | 62P99 | Model selection | Consistency | 62-07 | Theoretical and Computational Chemistry | Chemistry | Physical Chemistry | Comparisons | 62C99 | 26B05 | 26D07 | Math. Applications in Chemistry | MOLECULAR QUANTUM SIMILARITY | CRITERIA | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MATHEMATICAL-MODEL | VALIDATION | ERROR MEASURE | CHEMISTRY, MULTIDISCIPLINARY | Evaluation | Models | Statistical models | Statistics

Journal Article

Journal of Theoretical Probability, ISSN 0894-9840, 12/2018, Volume 31, Issue 4, pp. 2112 - 2128

Shafer’s belief functions were introduced in the seventies of the previous century as a mathematical tool in order to model epistemic probability. One of the...

Belief functions | Guaranteed revenue | Dutch Book | Behavioral interpretation | Mathematics(all) | Epistemic probability | Gamble | Statistics and Probability | Lower prevision | Axioms of probability | Statistics, Probability and Uncertainty | 60A55 | Probability Theory and Stochastic Processes | Mathematics | Statistics, general | 62C99 | STATISTICS & PROBABILITY | Translating and interpreting | Analysis | Distribution (Probability theory)

Belief functions | Guaranteed revenue | Dutch Book | Behavioral interpretation | Mathematics(all) | Epistemic probability | Gamble | Statistics and Probability | Lower prevision | Axioms of probability | Statistics, Probability and Uncertainty | 60A55 | Probability Theory and Stochastic Processes | Mathematics | Statistics, general | 62C99 | STATISTICS & PROBABILITY | Translating and interpreting | Analysis | Distribution (Probability theory)

Journal Article

Communications in Mathematics and Statistics, ISSN 2194-6701, 9/2019, Volume 7, Issue 3, pp. 309 - 328

We consider estimation of the scale parameter of a two-parameter exponential distribution on the basis of doubly censored data. Classes of estimators,...

Censored samples | Brewster–Zidek estimator | Stein-type estimator | 62C99 | Mathematics, general | 62F10 | Mathematics | Statistics, general | Inadmissibility | Equivariant estimator | MATHEMATICS | RISK EQUIVARIANT ESTIMATOR | Brewster-Zidek estimator

Censored samples | Brewster–Zidek estimator | Stein-type estimator | 62C99 | Mathematics, general | 62F10 | Mathematics | Statistics, general | Inadmissibility | Equivariant estimator | MATHEMATICS | RISK EQUIVARIANT ESTIMATOR | Brewster-Zidek estimator

Journal Article

Journal of Statistical Computation and Simulation, ISSN 0094-9655, 10/2018, Volume 88, Issue 15, pp. 2893 - 2908

Consider a linear regression model with some relevant regressors are unobservable. In such a situation, we estimate the model by using the proxy variables as...

positive-part estimator | dominance | 62J07 | predictive mean squared error | Shrinkage estimator | 62C99 | LINEAR-REGRESSION | ERRORS | PERFORMANCE | BIAS | RISK | STATISTICS & PROBABILITY | ORACLE PROPERTIES | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | LASSO | RELEVANT REGRESSORS | SELECTION | STEIN-RULE ESTIMATORS | Regression coefficients | Economic models | Regression models | Regression analysis | Shrinkage | Estimators

positive-part estimator | dominance | 62J07 | predictive mean squared error | Shrinkage estimator | 62C99 | LINEAR-REGRESSION | ERRORS | PERFORMANCE | BIAS | RISK | STATISTICS & PROBABILITY | ORACLE PROPERTIES | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | LASSO | RELEVANT REGRESSORS | SELECTION | STEIN-RULE ESTIMATORS | Regression coefficients | Economic models | Regression models | Regression analysis | Shrinkage | Estimators

Journal Article

15.
Full Text
On Asymptotically Optimal Tests under Loss of Identifiability in Semiparametric Models

The Annals of Statistics, ISSN 0090-5364, 10/2009, Volume 37, Issue 5A, pp. 2409 - 2444

We consider tests of hypotheses when the parameters are not identifiable under the null in semiparametric models, where regularity conditions for profile...

Maximum likelihood estimation | Identifiability | Null hypothesis | Statistical theories | Ratio test | Inference | Induced substructures | Semiparametric modeling | Parametric models | Statistics | Profile likelihood | Exponential average test | Odds-rate models | Contiguous alternative | Nonstandard testing problem | Optimal test | Empirical processes | Change-point models | Power | MIXTURE | contiguous alternative | odds-rate models | REGRESSION-MODELS | exponential average test | STATISTICS & PROBABILITY | COVARIATE | EFFICIENT ESTIMATION | nonstandard testing problem | INFERENCE | NUISANCE PARAMETER | PROPORTIONAL HAZARDS MODEL | LIKELIHOOD RATIO TESTS | CHANGE-POINT | profile likelihood | HYPOTHESIS | power | optimal test | empirical processes | 62A01 | 62C99 | 62G20 | 62G10

Maximum likelihood estimation | Identifiability | Null hypothesis | Statistical theories | Ratio test | Inference | Induced substructures | Semiparametric modeling | Parametric models | Statistics | Profile likelihood | Exponential average test | Odds-rate models | Contiguous alternative | Nonstandard testing problem | Optimal test | Empirical processes | Change-point models | Power | MIXTURE | contiguous alternative | odds-rate models | REGRESSION-MODELS | exponential average test | STATISTICS & PROBABILITY | COVARIATE | EFFICIENT ESTIMATION | nonstandard testing problem | INFERENCE | NUISANCE PARAMETER | PROPORTIONAL HAZARDS MODEL | LIKELIHOOD RATIO TESTS | CHANGE-POINT | profile likelihood | HYPOTHESIS | power | optimal test | empirical processes | 62A01 | 62C99 | 62G20 | 62G10

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2013, Volume 41, Issue 5, pp. 2292 - 2323

We consider the predictive problem of supervised ranking, where the task is to rank sets of candidate items returned in response to queries. Although there...

Aggregation | Sufficient conditions | Business orders | Assumption of risk | Directed acyclic graphs | Machine learning | Applied statistics | Conference proceedings | Uniform laws | Logistics | Fisher consistency | Ranking | Rank aggregation | Asymptotics | Consistency | U-statistics | CAPACITY | MINUS 2 | STATISTICS & PROBABILITY | rank aggregation | consistency | ONLINE | STATISTICAL-ANALYSIS | CLASSIFICATION METHODS | JUDGMENT | DECISION-MAKING | MINIMIZATION | asymptotics | PLUS | 62F07 | 62C99 | 62F12 | 68Q32

Aggregation | Sufficient conditions | Business orders | Assumption of risk | Directed acyclic graphs | Machine learning | Applied statistics | Conference proceedings | Uniform laws | Logistics | Fisher consistency | Ranking | Rank aggregation | Asymptotics | Consistency | U-statistics | CAPACITY | MINUS 2 | STATISTICS & PROBABILITY | rank aggregation | consistency | ONLINE | STATISTICAL-ANALYSIS | CLASSIFICATION METHODS | JUDGMENT | DECISION-MAKING | MINIMIZATION | asymptotics | PLUS | 62F07 | 62C99 | 62F12 | 68Q32

Journal Article

Electronic Journal of Statistics, ISSN 1935-7524, 2016, Volume 10, Issue 1, pp. 380 - 393

Scoring functions are used to evaluate and compare partially probabilistic forecasts. We investigate the use of rank-sum functions such as empirical Area Under...

Rank-sum | Scoring rule | Scoring function | Probabilistic prediction | Area under the curve | AREA | scoring function | ROC CURVE | scoring rule | STATISTICS & PROBABILITY | area under the curve | probabilistic prediction

Rank-sum | Scoring rule | Scoring function | Probabilistic prediction | Area under the curve | AREA | scoring function | ROC CURVE | scoring rule | STATISTICS & PROBABILITY | area under the curve | probabilistic prediction

Journal Article

Mathematical Methods of Statistics, ISSN 1066-5307, 4/2014, Volume 23, Issue 2, pp. 116 - 131

This paper deals with recovering an unknown vector β from the noisy data Y = Xβ + σξ, where X is a known n × p matrix with n ≥ p and ξ is a standard white...

spectral cut-off regularization | oracle inequality | secondary 62C10, 62C20, 62J05 | data-driven cut-off frequency | Statistical Theory and Methods | minimax risk | ill-posed linear model | primary 62C99 | Statistics

spectral cut-off regularization | oracle inequality | secondary 62C10, 62C20, 62J05 | data-driven cut-off frequency | Statistical Theory and Methods | minimax risk | ill-posed linear model | primary 62C99 | Statistics

Journal Article

Statistics, ISSN 0233-1888, 09/2017, Volume 51, Issue 5, pp. 1095 - 1104

Estimation of two normal means with an order restriction is considered when a covariance matrix is known. It is shown that restricted maximum likelihood...

mean squared error | Ordered normal means | stochastical domination | Pitman nearness criterion | MLE | 62F30 | 62C99 | 62F10 | 62H12 | RESTRICTED ESTIMATORS | STATISTICS & PROBABILITY

mean squared error | Ordered normal means | stochastical domination | Pitman nearness criterion | MLE | 62F30 | 62C99 | 62F10 | 62H12 | RESTRICTED ESTIMATORS | STATISTICS & PROBABILITY

Journal Article

EURO Journal on Decision Processes, ISSN 2193-9438, 11/2018, Volume 6, Issue 3, pp. 343 - 376

Scoring rules are traditional techniques to measure the association between a reported belief and an observed outcome. The condition that a scoring rule is...

Business and Management | Organization | 62C99 (Decision theory - None of the above, but in this section) | Operations Research/Decision Theory | Operations Research, Management Science | Comonotonicity | Cumulative prospect theory | Optimization | Proper scoring rules | Risk aversion | Scoring | Utilities | Expected utility | Decision theory | Weighting functions

Business and Management | Organization | 62C99 (Decision theory - None of the above, but in this section) | Operations Research/Decision Theory | Operations Research, Management Science | Comonotonicity | Cumulative prospect theory | Optimization | Proper scoring rules | Risk aversion | Scoring | Utilities | Expected utility | Decision theory | Weighting functions

Journal Article

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