Search Results - "algebra"

Proceedings of the 13th International Congress on Mathematical Education ICME-13 /

Table of Contents: “…Nemenzo, Masami Isoda, Maitree Inprasitha, Sampan Thinwiangthong, Narumon Changsri, Nisakorn Boonsena, Chan Roth, Monkolsery Lin, Souksomphone Anothay, Phoutsakhone Channgakham, Nguyen Chi Thanh, VŨ NHƯ Thư Hương, Phương Thảo Nguyễn -- Teaching and learning mathematics in Turkey, Huriye Arikan -- Part 6: Reports from the Topical Study Groups -- Topic study group no. 01 Early childhood mathematics education (up to age 7), Iliada Elia, Joanne Mulligan -- Topic study group no. 02 Mathematics education at tertiary level, Victor Giraldo, Chris Rasmussen -- Topic study group no. 03 Mathematics education in and for work, Diana Coben, Geoff Wake -- Topic study group no. 04 Activities for, and research on, mathematically gifted students, Florence Mihaela Singer, Linda Jensen Sheffield -- Topic study group no. 05 Classroom practice and research for students with mathematical learning difficulties, Lourdes Figueiras, Rose Griffiths -- Topic study group no. 06 Adult learning, Pradeep Kumar Misra, Jürgen Maaß -- Topic study group no. 07 Populariztion of Mathematics, Christian Mercat, Patrick Vennebush -- Topic study group no. 08 Teaching and learning of arithmetic and number systems (focus on primary education), Pi-Jen Lin, Terezinha Nunes -- Topic study group no. 09 Teaching and learning of measurement (focus on primary education), Christine Chambris, Barbara Dougherty -- Topic study group no. 10 Teaching and learning of early algebra, Carolyn Kieran, JeongSuk Pang -- Topic study group no. 11 Teachig and Learning of Algebra, Rakhi Banerjee, Amy Ellis -- Topic study group no. 12 Teaching and learning of geometry (primary level), Sinan Olkun, Ewa Swoboda -- Topic study group no. 13 Teaching and learning of geometry – secondary level, Ui Hock Cheah, Patricio G. …”
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Proceedings of the 13th International Congress on Mathematical Education ICME-13 /

Table of Contents: “…Nemenzo, Masami Isoda, Maitree Inprasitha, Sampan Thinwiangthong, Narumon Changsri, Nisakorn Boonsena, Chan Roth, Monkolsery Lin, Souksomphone Anothay, Phoutsakhone Channgakham, Nguyen Chi Thanh, VŨ NHƯ Thư Hương, Phương Thảo Nguyễn -- Teaching and learning mathematics in Turkey, Huriye Arikan -- Part 6: Reports from the Topical Study Groups -- Topic study group no. 01 Early childhood mathematics education (up to age 7), Iliada Elia, Joanne Mulligan -- Topic study group no. 02 Mathematics education at tertiary level, Victor Giraldo, Chris Rasmussen -- Topic study group no. 03 Mathematics education in and for work, Diana Coben, Geoff Wake -- Topic study group no. 04 Activities for, and research on, mathematically gifted students, Florence Mihaela Singer, Linda Jensen Sheffield -- Topic study group no. 05 Classroom practice and research for students with mathematical learning difficulties, Lourdes Figueiras, Rose Griffiths -- Topic study group no. 06 Adult learning, Pradeep Kumar Misra, Jürgen Maaß -- Topic study group no. 07 Populariztion of Mathematics, Christian Mercat, Patrick Vennebush -- Topic study group no. 08 Teaching and learning of arithmetic and number systems (focus on primary education), Pi-Jen Lin, Terezinha Nunes -- Topic study group no. 09 Teaching and learning of measurement (focus on primary education), Christine Chambris, Barbara Dougherty -- Topic study group no. 10 Teaching and learning of early algebra, Carolyn Kieran, JeongSuk Pang -- Topic study group no. 11 Teachig and Learning of Algebra, Rakhi Banerjee, Amy Ellis -- Topic study group no. 12 Teaching and learning of geometry (primary level), Sinan Olkun, Ewa Swoboda -- Topic study group no. 13 Teaching and learning of geometry – secondary level, Ui Hock Cheah, Patricio G. …”
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Scattering of acoustic and electromagnetic waves by small impedance bodies of arbitrary shapes : applications to creating new engineered materials / by Ramm, A. G. (Alexander G.)

Table of Contents: “…Many-body scattering problem in the case of small scatterers -- 6.1 Reduction of the problem to linear algebraic system -- 6.2 Derivation of the integral equation for the effective field -- 6.3 Summary of the results --…”
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Scattering of acoustic and electromagnetic waves by small impedance bodies of arbitrary shapes : applications to creating new engineered materials / by Ramm, A. G. (Alexander G.)

Table of Contents: “…Many-body scattering problem in the case of small scatterers -- 6.1 Reduction of the problem to linear algebraic system -- 6.2 Derivation of the integral equation for the effective field -- 6.3 Summary of the results --…”
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Mixed models theory and applications with R / by Demidenko, Eugene, 1948-

Table of Contents: “…331.16 Software and books361.17 Summary points 372 MLE for LME Model 412.1 Example: Weight versus height 422.2 The model and log-likelihood functions 452.3 Balanced random-coefficient model 602.4 LME model with random intercepts 642.5 Criterion for the MLE existence 722.6 Criterion for positive definiteness of matrix D742.7 Preestimation bounds for variance parameters 772.8 Maximization algorithms792.9 Derivatives of the log-likelihood function 812.10 Newton--Raphson algorithm 832.11 Fisher scoring algorithm852.12 EM algorithm 882.13 Starting point 932.14 Algorithms for restricted MLE 962.15 Optimization on nonnegative definite matrices 972.16 lmeFS and lme in R 1082.17 Appendix: Proof of the MLE existence 1122.18 Summary points 1153 Statistical Properties of the LME Model 1193.1 Introduction 1193.2 Identifiability of the LMEmodel 1193.3 Information matrix for variance parameters 1223.4 Profile-likelihood confidence intervals 1333.5 Statistical testing of the presence of random effects 1353.6 Statistical properties of MLE 1393.7 Estimation of random effects 1483.8 Hypothesis and membership testing 1533.9 Ignoring random effects 1573.10 MINQUE for variance parameters 1603.11 Method of moments 1693.12 Variance least squares estimator 1733.13 Projection on D+ space 1783.14 Comparison of the variance parameter estimation 1783.15 Asymptotically efficient estimation for [beta] 1823.16 Summary points 1834 Growth Curve Model and Generalizations 1874.1 Linear growth curve model 1874.2 General linear growth curve model 2034.3 Linear model with linear covariance structure 2214.4 Robust linear mixed effects model 2354.5 Appendix: Derivation of the MM estimator 2434.6 Summary points 2445 Meta-analysis Model 2475.1 Simple meta-analysis model 2485.2 Meta-analysis model with covariates 2755.3 Multivariate meta-analysis model 2805.4 Summary points 2916 Nonlinear Marginal Model 2936.1 Fixed matrix of random effects 2946.2 Varied matrix of random effects 3076.3 Three types of nonlinear marginal models 3186.4 Total generalized estimating equations approach 3236.5 Summary points 3307 Generalized Linear Mixed Models 3337.1 Regression models for binary data 3347.2 Binary model with subject-specific intercept 3577.3 Logistic regression with random intercept 3647.4 Probit model with random intercept 3847.5 Poisson model with random intercept 3887.6 Random intercept model: overview 4037.7 Mixed models with multiple random effects 4047.8 GLMM and simulation methods 4137.9 GEE for clustered marginal GLM 4187.10 Criteria for MLE existence for binary model 4267.11 Summary points 4318 Nonlinear Mixed Effects Model 4358.1 Introduction 4358.2 The model 4368.3 Example: Height of girls and boys 4398.4 Maximum likelihood estimation 4418.5 Two-stage estimator 4448.6 First-order approximation 4508.7 Lindstrom--Bates estimator 4528.8 Likelihood approximations 4578.9 One-parameter exponential model 4608.10 Asymptotic equivalence of the TS and LB estimators 4678.11 Bias-corrected two-stage estimator 4698.12 Distribution misspecification 4718.13 Partially nonlinear marginal mixed model 4748.14 Fixed sample likelihood approach4758.15 Estimation of random effects and hypothesis testing 4788.16 Example (continued) 4798.17 Practical recommendations 4818.18 Appendix: Proof of theorem on equivalence 4828.19 Summary points 4859 Diagnostics and Influence Analysis 4899.1 Introduction 4899.2 Influence analysis for linear regression 4909.3 The idea of infinitesimal influence 4939.4 Linear regression model 4959.5 Nonlinear regression model 5129.6 Logistic regression for binary outcome 5179.7 Influence of correlation structure 5269.8 Influence of measurement error 5279.9 Influence analysis for the LME model 5309.10 Appendix: MLE derivative with respect to σ2 5369.11 Summary points 53710 Tumor Regrowth Curves 54110.1 Survival curves 54310.2 Double--exponential regrowth curve 54510.3 Exponential growth with fixed regrowth time 55910.4 General regrowth curve 56510.5 Double--exponential transient regrowth curve 56610.6 Gompertz transient regrowth curve 57310.7 Summary points 57611 Statistical Analysis of Shape 57911.1 Introduction 57911.2 Statistical analysis of random triangles 58111.3 Face recognition 58411.4 Scale-irrelevant shape model 58511.5 Gorilla vertebrae analysis 58911.6 Procrustes estimation of the mean shape 59111.7 Fourier descriptor analysis 59811.8 Summary points 60712 Statistical Image Analysis 60912.1 Introduction 60912.2 Testing for uniform lighting 61212.3 Kolmogorov--Smirnov image comparison 61612.4 Multinomial statistical model for images 62012.5 Image entropy 62312.6 Ensemble of unstructured images 62712.7 Image alignment and registration 64012.8 Ensemble of structured images 65212.9 Modeling spatial correlation 65412.10 Summary points 66013 Appendix: Useful Facts and Formulas 66313.1 Basic facts of asymptotic theory 66313.2 Some formulas of matrix algebra 67013.3 Basic facts of optimization theory 674References 683Index 713.…”
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Mixed models theory and applications with R / by Demidenko, Eugene, 1948-

Table of Contents: “…331.16 Software and books361.17 Summary points 372 MLE for LME Model 412.1 Example: Weight versus height 422.2 The model and log-likelihood functions 452.3 Balanced random-coefficient model 602.4 LME model with random intercepts 642.5 Criterion for the MLE existence 722.6 Criterion for positive definiteness of matrix D742.7 Preestimation bounds for variance parameters 772.8 Maximization algorithms792.9 Derivatives of the log-likelihood function 812.10 Newton--Raphson algorithm 832.11 Fisher scoring algorithm852.12 EM algorithm 882.13 Starting point 932.14 Algorithms for restricted MLE 962.15 Optimization on nonnegative definite matrices 972.16 lmeFS and lme in R 1082.17 Appendix: Proof of the MLE existence 1122.18 Summary points 1153 Statistical Properties of the LME Model 1193.1 Introduction 1193.2 Identifiability of the LMEmodel 1193.3 Information matrix for variance parameters 1223.4 Profile-likelihood confidence intervals 1333.5 Statistical testing of the presence of random effects 1353.6 Statistical properties of MLE 1393.7 Estimation of random effects 1483.8 Hypothesis and membership testing 1533.9 Ignoring random effects 1573.10 MINQUE for variance parameters 1603.11 Method of moments 1693.12 Variance least squares estimator 1733.13 Projection on D+ space 1783.14 Comparison of the variance parameter estimation 1783.15 Asymptotically efficient estimation for [beta] 1823.16 Summary points 1834 Growth Curve Model and Generalizations 1874.1 Linear growth curve model 1874.2 General linear growth curve model 2034.3 Linear model with linear covariance structure 2214.4 Robust linear mixed effects model 2354.5 Appendix: Derivation of the MM estimator 2434.6 Summary points 2445 Meta-analysis Model 2475.1 Simple meta-analysis model 2485.2 Meta-analysis model with covariates 2755.3 Multivariate meta-analysis model 2805.4 Summary points 2916 Nonlinear Marginal Model 2936.1 Fixed matrix of random effects 2946.2 Varied matrix of random effects 3076.3 Three types of nonlinear marginal models 3186.4 Total generalized estimating equations approach 3236.5 Summary points 3307 Generalized Linear Mixed Models 3337.1 Regression models for binary data 3347.2 Binary model with subject-specific intercept 3577.3 Logistic regression with random intercept 3647.4 Probit model with random intercept 3847.5 Poisson model with random intercept 3887.6 Random intercept model: overview 4037.7 Mixed models with multiple random effects 4047.8 GLMM and simulation methods 4137.9 GEE for clustered marginal GLM 4187.10 Criteria for MLE existence for binary model 4267.11 Summary points 4318 Nonlinear Mixed Effects Model 4358.1 Introduction 4358.2 The model 4368.3 Example: Height of girls and boys 4398.4 Maximum likelihood estimation 4418.5 Two-stage estimator 4448.6 First-order approximation 4508.7 Lindstrom--Bates estimator 4528.8 Likelihood approximations 4578.9 One-parameter exponential model 4608.10 Asymptotic equivalence of the TS and LB estimators 4678.11 Bias-corrected two-stage estimator 4698.12 Distribution misspecification 4718.13 Partially nonlinear marginal mixed model 4748.14 Fixed sample likelihood approach4758.15 Estimation of random effects and hypothesis testing 4788.16 Example (continued) 4798.17 Practical recommendations 4818.18 Appendix: Proof of theorem on equivalence 4828.19 Summary points 4859 Diagnostics and Influence Analysis 4899.1 Introduction 4899.2 Influence analysis for linear regression 4909.3 The idea of infinitesimal influence 4939.4 Linear regression model 4959.5 Nonlinear regression model 5129.6 Logistic regression for binary outcome 5179.7 Influence of correlation structure 5269.8 Influence of measurement error 5279.9 Influence analysis for the LME model 5309.10 Appendix: MLE derivative with respect to σ2 5369.11 Summary points 53710 Tumor Regrowth Curves 54110.1 Survival curves 54310.2 Double--exponential regrowth curve 54510.3 Exponential growth with fixed regrowth time 55910.4 General regrowth curve 56510.5 Double--exponential transient regrowth curve 56610.6 Gompertz transient regrowth curve 57310.7 Summary points 57611 Statistical Analysis of Shape 57911.1 Introduction 57911.2 Statistical analysis of random triangles 58111.3 Face recognition 58411.4 Scale-irrelevant shape model 58511.5 Gorilla vertebrae analysis 58911.6 Procrustes estimation of the mean shape 59111.7 Fourier descriptor analysis 59811.8 Summary points 60712 Statistical Image Analysis 60912.1 Introduction 60912.2 Testing for uniform lighting 61212.3 Kolmogorov--Smirnov image comparison 61612.4 Multinomial statistical model for images 62012.5 Image entropy 62312.6 Ensemble of unstructured images 62712.7 Image alignment and registration 64012.8 Ensemble of structured images 65212.9 Modeling spatial correlation 65412.10 Summary points 66013 Appendix: Useful Facts and Formulas 66313.1 Basic facts of asymptotic theory 66313.2 Some formulas of matrix algebra 67013.3 Basic facts of optimization theory 674References 683Index 713.…”
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Mathematical horizons for quantum physics

An electronic book accessible through the World Wide Web; click to view

Mathematical horizons for quantum physics

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Contributions to functional syntax, semantics, and language comprehension

An electronic book accessible through the World Wide Web; click to view

Contributions to functional syntax, semantics, and language comprehension

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Data Science and Engineering

Open Access

Data Science and Engineering

Open Access