The Annals of Statistics, ISSN 0090-5364, 8/2009, Volume 37, Issue 4, pp. 1871 - 1905

We present a new methodology for sufficient dimension reduction (SDR). Our methodology derives directly from the formulation of SDR in terms of the conditional...

Dimensionality reduction | Datasets | Covariance | Logical givens | Hilbert spaces | Mathematical vectors | Matrices | Random variables | Estimators | Estimation methods | Regression | Dimension reduction | Reproducing kernel | Positive definite kernel | Consistency | VISUALIZATION | regression | STATISTICS & PROBABILITY | positive definite kernel | SLICED INVERSE REGRESSION | reproducing kernel | consistency | 62J02 | 62H99

Dimensionality reduction | Datasets | Covariance | Logical givens | Hilbert spaces | Mathematical vectors | Matrices | Random variables | Estimators | Estimation methods | Regression | Dimension reduction | Reproducing kernel | Positive definite kernel | Consistency | VISUALIZATION | regression | STATISTICS & PROBABILITY | positive definite kernel | SLICED INVERSE REGRESSION | reproducing kernel | consistency | 62J02 | 62H99

Journal Article

The Annals of Statistics, ISSN 0090-5364, 6/2004, Volume 32, Issue 3, pp. 928 - 961

A class of variable selection procedures for parametric models via non-concave penalized likelihood was proposed by Fan and Li to simultaneously estimate...

Penalty function | Sample size | Threshing | Model Selection | Mathematical independent variables | Standard error | Statistics | Parametric models | Modeling | Estimators | Oracles | Likelihood ratio statistic | Nonconcave penalized likelihood | Asymptotic normality | Model selection | Oracle property | Diverging parameters | Standard errors | REGRESSION | oracle property | WALD MEMORIAL LECTURES | model selection | STATISTICS & PROBABILITY | nonconcave penalized likelihood | VARIABLE SELECTION | standard errors | asymptotic normality | likelihood ratio statistic | diverging parameters | SHRINKAGE | ESTIMATORS | LASSO | ASYMPTOTICS | EFFICIENCY | 62J02 | 62E20 | 62F12

Penalty function | Sample size | Threshing | Model Selection | Mathematical independent variables | Standard error | Statistics | Parametric models | Modeling | Estimators | Oracles | Likelihood ratio statistic | Nonconcave penalized likelihood | Asymptotic normality | Model selection | Oracle property | Diverging parameters | Standard errors | REGRESSION | oracle property | WALD MEMORIAL LECTURES | model selection | STATISTICS & PROBABILITY | nonconcave penalized likelihood | VARIABLE SELECTION | standard errors | asymptotic normality | likelihood ratio statistic | diverging parameters | SHRINKAGE | ESTIMATORS | LASSO | ASYMPTOTICS | EFFICIENCY | 62J02 | 62E20 | 62F12

Journal Article

The Annals of Statistics, ISSN 0090-5364, 6/2012, Volume 40, Issue 3, pp. 1846 - 1877

Independence screening is a variable selection method that uses a ranking criterion to select significant variables, particularly for statistical models with...

Generalized linear model | Gaussian distributions | Sample size | Correlations | Mathematical transformations | Correlation coefficients | Regression analysis | Modeling | Linear models | Estimators | Dimensionality reduction | Rank correlation screening | Semiparametric models | Large p small n | SIS | Variable selection | large p small n | NONCONCAVE PENALIZED LIKELIHOOD | dimensionality reduction | semiparametric models | DIVERGING NUMBER | STATISTICS & PROBABILITY | SLICED INVERSE REGRESSION | rank correlation screening | GENERALIZED LINEAR-MODELS | 62J02 | 62F07 | 62F35 | 62J12

Generalized linear model | Gaussian distributions | Sample size | Correlations | Mathematical transformations | Correlation coefficients | Regression analysis | Modeling | Linear models | Estimators | Dimensionality reduction | Rank correlation screening | Semiparametric models | Large p small n | SIS | Variable selection | large p small n | NONCONCAVE PENALIZED LIKELIHOOD | dimensionality reduction | semiparametric models | DIVERGING NUMBER | STATISTICS & PROBABILITY | SLICED INVERSE REGRESSION | rank correlation screening | GENERALIZED LINEAR-MODELS | 62J02 | 62F07 | 62F35 | 62J12

Journal Article

The Annals of Statistics, ISSN 0090-5364, 12/2010, Volume 38, Issue 6, pp. 3811 - 3836

In partially linear single-index models, we obtain the semiparametrically efficient profile least-squares estimators of regression coefficients. We also employ...

Linear regression | Logical proofs | Mathematical independent variables | Coefficients | Parametric models | Modeling | Linear models | Estimators | Consistent estimators | Oracles | Hypothesis testing | Profile likelihood | Efficiency | SCAD | Local linear regression | Nonparametric regression | REGRESSION | nonparametric regression | SEMIPARAMETRIC ESTIMATION | local linear regression | hypothesis testing | profile likelihood | STATISTICS & PROBABILITY | VARIANCE | VARIABLE SELECTION | 62J02 | 62G08 | 62G20 | 62G10 | 62F12

Linear regression | Logical proofs | Mathematical independent variables | Coefficients | Parametric models | Modeling | Linear models | Estimators | Consistent estimators | Oracles | Hypothesis testing | Profile likelihood | Efficiency | SCAD | Local linear regression | Nonparametric regression | REGRESSION | nonparametric regression | SEMIPARAMETRIC ESTIMATION | local linear regression | hypothesis testing | profile likelihood | STATISTICS & PROBABILITY | VARIANCE | VARIABLE SELECTION | 62J02 | 62G08 | 62G20 | 62G10 | 62F12

Journal Article

Probability Theory and Related Fields, ISSN 0178-8051, 10/2017, Volume 169, Issue 1, pp. 523 - 564

This paper considers the problem of estimation of a low-rank matrix when most of its entries are not observed and some of the observed entries are corrupted....

62G05 | 62J02 | Mathematical and Computational Biology | Theoretical, Mathematical and Computational Physics | Operations Research/Decision Theory | Probability Theory and Stochastic Processes | Mathematics | Quantitative Finance | 62G35 | DECOMPOSITION | STATISTICS & PROBABILITY | OPTIMAL RATES | Sparsity | Minimax technique | Convexity | Statistics

62G05 | 62J02 | Mathematical and Computational Biology | Theoretical, Mathematical and Computational Physics | Operations Research/Decision Theory | Probability Theory and Stochastic Processes | Mathematics | Quantitative Finance | 62G35 | DECOMPOSITION | STATISTICS & PROBABILITY | OPTIMAL RATES | Sparsity | Minimax technique | Convexity | Statistics

Journal Article

6.
Full Text
Interaction pursuit in high-dimensional multi-response regression via distance correlation

Annals of Statistics, ISSN 0090-5364, 04/2017, Volume 45, Issue 2, pp. 897 - 922

Feature interactions can contribute to a large proportion of variation in many prediction models. In the era of big data, the coexistence of high...

Distance correlation | High dimensionality | Sparsity | Multiresponse regression | Square transformation | Interaction pursuit | square transformation | multi-response regression | REDUCTION | high dimensionality | STATISTICS & PROBABILITY | sparsity | VARIABLE SELECTION | distance correlation

Distance correlation | High dimensionality | Sparsity | Multiresponse regression | Square transformation | Interaction pursuit | square transformation | multi-response regression | REDUCTION | high dimensionality | STATISTICS & PROBABILITY | sparsity | VARIABLE SELECTION | distance correlation

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2009, Volume 37, Issue 5A, pp. 2245 - 2277

We consider regression models with parametric (linear or nonlinear) regression function and allow responses to be "missing at random." We assume that the...

Approximation | Linear regression | Least squares | Inference | Mathematical moments | Regression analysis | Parametric models | Estimators | Consistent estimators | Confidence interval | Empirical likelihood | Gradient | Influence function | Semiparametric regression | Weighted empirical estimator | IMPUTATION | gradient | STATISTICS & PROBABILITY | EFFICIENT ESTIMATION | influence function | empirical likelihood | MODELS | MEAN FUNCTIONALS | EMPIRICAL-LIKELIHOOD | confidence interval | LIKELIHOOD-BASED INFERENCE | weighted empirical estimator | 62J02 | 62N01 | 62G20 | 62F12

Approximation | Linear regression | Least squares | Inference | Mathematical moments | Regression analysis | Parametric models | Estimators | Consistent estimators | Confidence interval | Empirical likelihood | Gradient | Influence function | Semiparametric regression | Weighted empirical estimator | IMPUTATION | gradient | STATISTICS & PROBABILITY | EFFICIENT ESTIMATION | influence function | empirical likelihood | MODELS | MEAN FUNCTIONALS | EMPIRICAL-LIKELIHOOD | confidence interval | LIKELIHOOD-BASED INFERENCE | weighted empirical estimator | 62J02 | 62N01 | 62G20 | 62F12

Journal Article

Journal of Applied Mathematics, Statistics and Informatics, ISSN 1339-0015, 12/2019, Volume 15, Issue 2, pp. 47 - 59

We are interested in comparing the performance of various nonlinear estimators of parameters of the standard nonlinear regression model. While the standard...

62J02 | computations | 68T05 | optimization | optimal method selection | nonlinear least weighted squares | 68-04 | 62G35 | 62j02 | 68t05 | 62g35

62J02 | computations | 68T05 | optimization | optimal method selection | nonlinear least weighted squares | 68-04 | 62G35 | 62j02 | 68t05 | 62g35

Journal Article

The Annals of Statistics, ISSN 0090-5364, 2/2008, Volume 36, Issue 1, pp. 167 - 198

We propose a test for model specification of a parametric diffusion process based on a kernel estimation of the transitional density of the process. The...

Density estimation | Economic models | Time series models | Statistical theories | Data smoothing | Nonparametric models | Parametric models | Statistics | Density | Estimators | Diffusion process | Transitional density | Bootstrap | Empirical likelihood | Goodness-of-fit test | Time series | REGRESSION | MAXIMUM-LIKELIHOOD-ESTIMATION | transitional density | time series | STATISTICS & PROBABILITY | goodness-of-fit test | OF-FIT TESTS | bootstrap | empirical likelihood | CONTINUOUS-TIME MODELS | TERM STRUCTURE | DENSITIES | NONPARAMETRIC-ESTIMATION | diffusion process | 62G05 | 62J02

Density estimation | Economic models | Time series models | Statistical theories | Data smoothing | Nonparametric models | Parametric models | Statistics | Density | Estimators | Diffusion process | Transitional density | Bootstrap | Empirical likelihood | Goodness-of-fit test | Time series | REGRESSION | MAXIMUM-LIKELIHOOD-ESTIMATION | transitional density | time series | STATISTICS & PROBABILITY | goodness-of-fit test | OF-FIT TESTS | bootstrap | empirical likelihood | CONTINUOUS-TIME MODELS | TERM STRUCTURE | DENSITIES | NONPARAMETRIC-ESTIMATION | diffusion process | 62G05 | 62J02

Journal Article

The Annals of Statistics, ISSN 0090-5364, 12/2004, Volume 32, Issue 6, pp. 2469 - 2500

A local linear kernel estimator of the regression function , x ∈ R , of a stationary (d + 1)-dimensional spatial process observed over a rectangular domain of...

Integers | Density estimation | Regression Analysis | Sample size | Infinity | Linear regression | Time series | Polynomials | Random variables | Spatial models | Estimators | Mixing random field | Local linear kernel estimate | Asymptotic normality | Spatial regression | MIXING SEQUENCES | STATISTICS & PROBABILITY | VARIABLE BANDWIDTH | mixing random field | RANDOM-FIELDS | KERNEL DENSITY-ESTIMATION | STRONG CONSISTENCY | asymptotic normality | STATIONARY-PROCESSES | CENTRAL LIMIT-THEOREM | NONPARAMETRIC-ESTIMATION | local linear kernel estimate | spatial regression | NONLINEAR TIME-SERIES | 62G05 | 62J02 | 60J25

Integers | Density estimation | Regression Analysis | Sample size | Infinity | Linear regression | Time series | Polynomials | Random variables | Spatial models | Estimators | Mixing random field | Local linear kernel estimate | Asymptotic normality | Spatial regression | MIXING SEQUENCES | STATISTICS & PROBABILITY | VARIABLE BANDWIDTH | mixing random field | RANDOM-FIELDS | KERNEL DENSITY-ESTIMATION | STRONG CONSISTENCY | asymptotic normality | STATIONARY-PROCESSES | CENTRAL LIMIT-THEOREM | NONPARAMETRIC-ESTIMATION | local linear kernel estimate | spatial regression | NONLINEAR TIME-SERIES | 62G05 | 62J02 | 60J25

Journal Article

The Annals of Statistics, ISSN 0090-5364, 4/2014, Volume 42, Issue 2, pp. 592 - 624

We propose generalized additive partial linear models for complex data which allow one to capture nonlinear patterns of some covariates, in the presence of...

Sample size | Correlations | Inference | Polynomials | Nonparametric models | Linear models | Parametric models | Estimators | Oracles | Estimation methods | Model selection | Oracle property | SCAD | Polynomial splines | Group selection | Quadratic inference function | Partial linear models | Additive model | Selection consistency | REGRESSION | oracle property | model selection | STATISTICS & PROBABILITY | LONGITUDINAL DATA | group selection | INFERENCE | selection consistency | VARIABLE SELECTION | NONCONCAVE PENALIZED LIKELIHOOD | VARYING-COEFFICIENT MODELS | quadratic inference function | ASYMPTOTICS | polynomial splines | SPLINE | partial linear models | 62J02 | 62G08 | 62G20 | 62G10 | 62F12

Sample size | Correlations | Inference | Polynomials | Nonparametric models | Linear models | Parametric models | Estimators | Oracles | Estimation methods | Model selection | Oracle property | SCAD | Polynomial splines | Group selection | Quadratic inference function | Partial linear models | Additive model | Selection consistency | REGRESSION | oracle property | model selection | STATISTICS & PROBABILITY | LONGITUDINAL DATA | group selection | INFERENCE | selection consistency | VARIABLE SELECTION | NONCONCAVE PENALIZED LIKELIHOOD | VARYING-COEFFICIENT MODELS | quadratic inference function | ASYMPTOTICS | polynomial splines | SPLINE | partial linear models | 62J02 | 62G08 | 62G20 | 62G10 | 62F12

Journal Article

The Annals of Statistics, ISSN 0090-5364, 6/2014, Volume 42, Issue 3, pp. 872 - 917

Most papers on high-dimensional statistics are based on the assumption that none of the regressors are correlated with the regression error, namely, they are...

Economic models | Penalty function | Linear regression | Least squares | Instrumental variables estimation | Instrumental variables | Linear models | Estimators | Consistent estimators | Oracles | Global minimization | Oracle property | Focused GMM | Conditional moment restriction | Semiparametric efficiency | Estimating equation | Sparsity recovery | Over identification | Endogenous variables | semi-parametric efficiency | DANTZIG SELECTOR | REGRESSION-MODELS | oracle property | endogenous variables | STATISTICS & PROBABILITY | EFFICIENT ESTIMATION | global minimization | VARIABLE SELECTION | NONPARAMETRIC MODELS | GENERALIZED-METHOD | estimating equation | ORACLE PROPERTIES | sparsity recovery | NONCONCAVE PENALIZED LIKELIHOOD | conditional moment restriction | over identification | CONDITIONAL MOMENT RESTRICTIONS | ADAPTIVE ELASTIC-NET | 62J02 | 62P20 | semiparametric efficiency | 62F12 | 62J12 | Semi-parametric efficiency

Economic models | Penalty function | Linear regression | Least squares | Instrumental variables estimation | Instrumental variables | Linear models | Estimators | Consistent estimators | Oracles | Global minimization | Oracle property | Focused GMM | Conditional moment restriction | Semiparametric efficiency | Estimating equation | Sparsity recovery | Over identification | Endogenous variables | semi-parametric efficiency | DANTZIG SELECTOR | REGRESSION-MODELS | oracle property | endogenous variables | STATISTICS & PROBABILITY | EFFICIENT ESTIMATION | global minimization | VARIABLE SELECTION | NONPARAMETRIC MODELS | GENERALIZED-METHOD | estimating equation | ORACLE PROPERTIES | sparsity recovery | NONCONCAVE PENALIZED LIKELIHOOD | conditional moment restriction | over identification | CONDITIONAL MOMENT RESTRICTIONS | ADAPTIVE ELASTIC-NET | 62J02 | 62P20 | semiparametric efficiency | 62F12 | 62J12 | Semi-parametric efficiency

Journal Article

The Annals of Statistics, ISSN 0090-5364, 10/2014, Volume 42, Issue 5, pp. 1751 - 1786

Variable selection, also known as feature selection in machine learning, plays an important role in modeling high dimensional data and is key to datadriven...

Datasets | Dimensionality reduction | Statistical discrepancies | Sample size | False positive errors | Gene expression | Modeling | Statistical relevance model | Linear models | Parametric models | Inverse models | Interactions | Sure independence screening | Sliced inverse regression | Variable selection | inverse models | STATISTICS & PROBABILITY | CANCER | ORACLE PROPERTIES | DISCOVERY | SUFFICIENT DIMENSION REDUCTION | sliced inverse regression | sure independence screening | EMBRYONIC STEM-CELLS | LASSO | variable selection | EXPRESSION | LIKELIHOOD | Statistics - Methodology | 62J02 | 62P10 | 62H25

Datasets | Dimensionality reduction | Statistical discrepancies | Sample size | False positive errors | Gene expression | Modeling | Statistical relevance model | Linear models | Parametric models | Inverse models | Interactions | Sure independence screening | Sliced inverse regression | Variable selection | inverse models | STATISTICS & PROBABILITY | CANCER | ORACLE PROPERTIES | DISCOVERY | SUFFICIENT DIMENSION REDUCTION | sliced inverse regression | sure independence screening | EMBRYONIC STEM-CELLS | LASSO | variable selection | EXPRESSION | LIKELIHOOD | Statistics - Methodology | 62J02 | 62P10 | 62H25

Journal Article

Structural and Multidisciplinary Optimization, ISSN 1615-147X, 7/2019, Volume 60, Issue 1, pp. 245 - 268

In this paper, the minimization of computational cost on evaluating multidimensional integrals is explored. More specifically, a method based on an adaptive...

Engineering | Computational Mathematics and Numerical Analysis | Efficient global optimization | Robust optimization | Stochastic kriging | Adaptive target variance | Integral minimization | Engineering Design | Theoretical and Applied Mechanics

Engineering | Computational Mathematics and Numerical Analysis | Efficient global optimization | Robust optimization | Stochastic kriging | Adaptive target variance | Integral minimization | Engineering Design | Theoretical and Applied Mechanics

Journal Article

The Annals of Statistics, ISSN 0090-5364, 2/2013, Volume 41, Issue 1, pp. 250 - 268

We develop an efficient estimation procedure for identifying and estimating the central subspace. Using a new way of parameterization, we convert the problem...

Dimensionality reduction | Efficiency metrics | Standard error | Matrices | Parameterization | Interval estimators | Estimators | Consistent estimators | Estimation methods | Oracles | Semiparametric efficiency | Dimension reduction | Central subspace | Estimating equations | Sliced inverse regression | MODELS | sliced inverse regression | MEASUREMENT ERROR | semiparametric efficiency | DISTRIBUTED PREDICTORS | estimating equations | SEMIPARAMETRIC ESTIMATORS | STATISTICS & PROBABILITY | dimension reduction | 62J02 | 62F12 | 62H12

Dimensionality reduction | Efficiency metrics | Standard error | Matrices | Parameterization | Interval estimators | Estimators | Consistent estimators | Estimation methods | Oracles | Semiparametric efficiency | Dimension reduction | Central subspace | Estimating equations | Sliced inverse regression | MODELS | sliced inverse regression | MEASUREMENT ERROR | semiparametric efficiency | DISTRIBUTED PREDICTORS | estimating equations | SEMIPARAMETRIC ESTIMATORS | STATISTICS & PROBABILITY | dimension reduction | 62J02 | 62F12 | 62H12

Journal Article

The Annals of Statistics, ISSN 0090-5364, 2/2015, Volume 43, Issue 1, pp. 30 - 56

We develop general theory for finding locally optimal designs in a class of single-covariate models under any differentiable optimality criterion. Yang and...

Generalized linear model | Chebyshev system | Nonlinear model | Complete class | Locally optimal design | generalized linear model | complete class | NONLINEAR MODELS | REGRESSION-MODELS | locally optimal design | nonlinear model | STATISTICS & PROBABILITY | LA GARZA PHENOMENON | POINTS | 62J02 | 62K05

Generalized linear model | Chebyshev system | Nonlinear model | Complete class | Locally optimal design | generalized linear model | complete class | NONLINEAR MODELS | REGRESSION-MODELS | locally optimal design | nonlinear model | STATISTICS & PROBABILITY | LA GARZA PHENOMENON | POINTS | 62J02 | 62K05

Journal Article

Electronic Journal of Statistics, ISSN 1935-7524, 2008, Volume 2, pp. 1153 - 1194

Journal Article

Mathematical Programming, ISSN 0025-5610, 7/2018, Volume 170, Issue 1, pp. 97 - 119

Symbolic regression methods generate expression trees that simultaneously define the functional form of a regression model and the regression parameter values....

62J02 | Symbolic regression | 65K05 | 68T05 | Theoretical, Mathematical and Computational Physics | Integer nonlinear optimization | Mathematics | 90C26 | Mathematical Methods in Physics | Global optimization | Calculus of Variations and Optimal Control; Optimization | Mathematics of Computing | Numerical Analysis | Machine learning | Combinatorics | 68Q99 | COMPUTER SCIENCE, SOFTWARE ENGINEERING | MIXED-INTEGER | MATHEMATICS, APPLIED | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | OPTIMIZATION | INTEGER NONLINEAR PROGRAMS | SELECTION | Analysis | Algorithms | Operators (mathematics) | Regression models | Subtraction | Adaptive algorithms | Solvers | Division | Regression analysis | Nonlinear programming | Optimization | Genetic algorithms

62J02 | Symbolic regression | 65K05 | 68T05 | Theoretical, Mathematical and Computational Physics | Integer nonlinear optimization | Mathematics | 90C26 | Mathematical Methods in Physics | Global optimization | Calculus of Variations and Optimal Control; Optimization | Mathematics of Computing | Numerical Analysis | Machine learning | Combinatorics | 68Q99 | COMPUTER SCIENCE, SOFTWARE ENGINEERING | MIXED-INTEGER | MATHEMATICS, APPLIED | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | OPTIMIZATION | INTEGER NONLINEAR PROGRAMS | SELECTION | Analysis | Algorithms | Operators (mathematics) | Regression models | Subtraction | Adaptive algorithms | Solvers | Division | Regression analysis | Nonlinear programming | Optimization | Genetic algorithms

Journal Article

Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena, ISSN 0960-0779, 06/2015, Volume 75, pp. 134 - 152

Most of the known methods for estimating the fractal dimension of fractal sets are based on the evaluation of a single geometric characteristic, e.g. the...

MULTIFRACTAL ANALYSIS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | SURFACE-AREA | LACUNARITY | PHYSICS, MULTIDISCIPLINARY | MINKOWSKI FUNCTIONALS | COMPUTATION | PHYSICS, MATHEMATICAL | POLYCONVEX SETS

MULTIFRACTAL ANALYSIS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | SURFACE-AREA | LACUNARITY | PHYSICS, MULTIDISCIPLINARY | MINKOWSKI FUNCTIONALS | COMPUTATION | PHYSICS, MATHEMATICAL | POLYCONVEX SETS

Journal Article

Complex Analysis and Operator Theory, ISSN 1661-8254, 9/2019, Volume 13, Issue 6, pp. 2799 - 2811

We consider the reconstruction of spike train signals of the form $$\begin{aligned} F(x) = \sum _{i=1}^d a_i \delta (x-x_i), \end{aligned}$$ F ( x ) = ∑ i = 1...

62J02 | 42C99 | Operator Theory | Singularities | Analysis | Moments inversion | 14P10 | Non-linear models | Mathematics, general | Mathematics | 94A12 | Signal acquisition | MATHEMATICS | MATHEMATICS, APPLIED | RECONSTRUCTION | EXPONENTIALS | SUMS | Models | Algorithms

62J02 | 42C99 | Operator Theory | Singularities | Analysis | Moments inversion | 14P10 | Non-linear models | Mathematics, general | Mathematics | 94A12 | Signal acquisition | MATHEMATICS | MATHEMATICS, APPLIED | RECONSTRUCTION | EXPONENTIALS | SUMS | Models | Algorithms

Journal Article

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