LEADER 04267nam 22006975 450 001 9910983324503321 005 20250117143724.0 010 $a9783031752476 010 $a3031752473 024 7 $a10.1007/978-3-031-75247-6 035 $a(CKB)37178031900041 035 $a(MiAaPQ)EBC31876172 035 $a(Au-PeEL)EBL31876172 035 $a(DE-He213)978-3-031-75247-6 035 $a(OCoLC)1485006137 035 $a(EXLCZ)9937178031900041 100 $a20250109d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aWaste-Derived Carbon Nanostructures $eSynthesis and Applications /$fedited by Neetu Talreja, Divya Chauhan, Mohammad Ashfaq 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (479 pages) 225 1 $aNanostructure Science and Technology,$x2197-7976 311 08$a9783031752469 311 08$a3031752465 327 $aWaste-Derived Carbon Nanomaterials (WD-CBNMs): Synthesis and Characterization -- Agrowaste-Derived 'Natural' Carbon Nanomaterial with Versatile Applications: Bacterial Cellulose -- Synthesis of carbon nanomaterials from agro-industrial wastes and their extensive applications -- Biological waste-derived carbon dots and their applications -- Waste-derived Cellulose Nanomaterials Based Membranes for Water Filtration Application -- Waste-derived Graphene for the removal of heavy metals: A sustainable approach towards environmental remediation -- Rice Waste-derived Carbon Nanomaterials for Environmental Applications -- Nutshell-derived efficient carbon nanomaterials as a potential smart electrode material for electrocatalytic hydrogen production -- Agricultural waste derived carbon nanomaterials for biomedical applications -- Synthesis and Characterization of Bio-based Carbon Nanomaterials from Agricultural Waste for Tissue Engineering Application -- Waste driven Carbon Nanomaterials for drug delivery application -- Waste derived carbon nanotubes (CNTs): A revolutionary product towards energy applications -- Waste-derived Carbon Nanomaterials for Solar Cell Applications -- Waste-derived carbon nanomaterials for Microbial Fuel Cells -- Waste-derived Graphene: A new avenue for Supercapacitors. 330 $aThis contributed volume focuses on the development of waste-derived carbon nanostructures (WD-CNs) from various waste materials, such as municipal garbage, plastics, industrial waste, and agricultural residues, highlighting their potential for recycling in a circular economy. It explores synthetic processes that convert waste into valuable carbon nanomaterials, reducing the need for cleansing and lowering the carbon footprint compared to traditional methods. The book also examines the functionalization of WD-CNs for diverse applications in energy, environment, and biology, promoting sustainable innovation and commercialization of green technologies. It is a useful tool for researchers, graduate students and professionals working in the fields of materials science, nanotechnology, environmental science, and chemical engineering. . 410 0$aNanostructure Science and Technology,$x2197-7976 606 $aNanotechnology 606 $aMaterials 606 $aCatalysis 606 $aForce and energy 606 $aSustainability 606 $aNanoscale Design, Synthesis and Processing 606 $aMaterials for Energy and Catalysis 606 $aNanotechnology 606 $aSustainability 615 0$aNanotechnology. 615 0$aMaterials. 615 0$aCatalysis. 615 0$aForce and energy. 615 0$aSustainability. 615 14$aNanoscale Design, Synthesis and Processing. 615 24$aMaterials for Energy and Catalysis. 615 24$aNanotechnology. 615 24$aSustainability. 676 $a620.5 700 $aTalreja$b Neetu$01591960 701 $aChauhan$b Divya$01591961 701 $aAshfaq$b Mohammad$01591962 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983324503321 996 $aWaste-Derived Carbon Nanostructures$94316232 997 $aUNINA LEADER 13773nam 22006375 450 001 9910805583103321 005 20250527153039.0 010 $a9783031419881 010 $a303141988X 024 7 $a10.1007/978-3-031-41988-1 035 $a(CKB)30098002000041 035 $a(MiAaPQ)EBC31086001 035 $a(Au-PeEL)EBL31086001 035 $a(DE-He213)978-3-031-41988-1 035 $a(MiAaPQ)EBC31093934 035 $a(Au-PeEL)EBL31093934 035 $a(EXLCZ)9930098002000041 100 $a20240124d2023 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling /$fby George J. Knafl 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (525 pages) 311 08$a9783031419874 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Acknowledgments -- About This Book -- Contents -- About the Author -- Abbreviations -- Chapter 1: Introduction -- 1.1 Background -- 1.2 Overview of Part I -- 1.3 Overview of Part II -- 1.4 Overview of Part III -- References -- Part I: Continuous, Count, and Dichotomous Outcomes -- Chapter 2: Standard GEE Modeling of Correlated Univariate Outcomes -- 2.1 Correlated Univariate Outcomes -- 2.2 Generalized Linear Modeling -- 2.2.1 Linear Regression with Identity Link Function -- 2.2.2 Poisson Regression with Natural Log Link Function -- 2.2.3 Logistic Regression with Logit Link Function -- 2.2.4 Exponential Regression with Natural Log Link Function -- 2.3 Modeling Correlations -- 2.3.1 Independent Correlations -- 2.3.2 Exchangeable Correlations -- 2.3.3 Autoregressive Order 1 Correlations -- 2.3.4 Unstructured Correlations -- 2.4 Standard GEE Modeling -- 2.4.1 Estimating the Correlation Structure -- 2.4.2 Estimating the Covariance Matrix for Mean Parameter Estimates -- 2.4.3 Parameter Estimation Problems -- 2.5 The Likelihood Function -- 2.6 Likelihood Cross-Validation -- 2.6.1 Choosing the Number of Folds -- 2.6.2 LCV Ratio Tests -- 2.6.3 Penalized Likelihood Criteria -- 2.7 Adaptive Regression Modeling of Means -- 2.8 Example Data Sets -- 2.8.1 The Dental Measurement Data -- 2.8.2 The Epilepsy Seizure Rate Data -- 2.8.3 The Dichotomous Respiratory Status Data -- 2.8.4 The Blood Lead Level Data -- References -- Chapter 3: Partially Modified GEE Modeling of Correlated Univariate Outcomes -- 3.1 Including Non-constant Dispersions -- 3.2 Adding Estimating Equations for the Dispersions Based on the Likelihood -- 3.3 Estimating the Correlation Structure -- 3.4 Estimating the Covariance Matrix for Coefficient Parameter Estimates -- 3.5 The Constant Dispersion Model -- 3.6 Degeneracy in Correlation Parameter Estimation. 327 $a3.7 The Estimation Process -- 3.7.1 Step 1 Adjustment -- 3.7.2 Step 2 Adjustment -- 3.7.3 Stopping the Estimation Process -- 3.7.4 Initial Estimates -- 3.7.5 Other Computational Issues -- 3.7.6 Recommended Tolerance Settings -- 3.8 Variation in Measurement Conditions -- References -- Chapter 4: Fully Modified GEE Modeling of Correlated Univariate Outcomes -- 4.1 Estimating Equations for Means and Dispersions Based on the Likelihood -- 4.2 Alternate Regression Types -- 4.2.1 Linear Regression with Identity Link Function -- 4.2.2 Poisson Regression with Natural Log Link Function -- 4.2.3 Logistic Regression with Logit Link Function -- 4.2.4 Exponential Regression with Natural Log Link Function -- 4.2.5 Inverse Gaussian Regression with Natural Log Link Function -- 4.3 The Parameter Estimation Process -- 4.3.1 Revised Stopping Criteria -- 4.3.2 Initial Estimates -- 4.4 Singleton Univariate Outcomes -- References -- Chapter 5: Extended Linear Mixed Modeling of Correlated Univariate Outcomes -- 5.1 Estimating Equations for Means, Dispersions, and Correlations Based on the Likelihood -- 5.2 Adjustments to the Estimation Process -- 5.3 Exchangeable Correlation Structure Computations -- 5.3.1 A General Class of Symmetric Matrices -- 5.3.2 Eigenvalues of the EC Correlation Matrix -- 5.3.3 Inverse of the EC Correlation Matrix -- 5.3.4 Square Root of the EC Correlation Matrix -- 5.3.5 Inverse of the Square Root of the EC Correlation Matrix -- 5.3.6 Derivatives with Respect to the Constant EC Correlation -- 5.4 Spatial Autoregressive Order 1 Correlation Structure Computations -- 5.4.1 Square Root and Determinant of the Spatial AR1 Correlation Matrix -- 5.4.2 Inverse of the Square Root of the Spatial AR1 Correlation Matrix -- 5.4.3 Derivatives with Respect to the Spatial Autocorrelation -- 5.5 Unstructured Correlation Structure Computations. 327 $a5.6 Verifying Gradient and Hessian Computations -- 5.7 Direct Variance Modeling -- References -- Chapter 6: Example Analyses of the Dental Measurement Data -- 6.1 Choosing the Number of Folds and the Correlation Structure -- 6.2 Assessing Linearity of Means in Child Age -- 6.3 Comparison to Standard GEE Modeling -- 6.4 Modeling Means and Variances in Child Age -- 6.5 Adaptive Additive Models in Child Age and Child Gender -- 6.6 Adaptive Moderation of the Effect of Child Age by Child Gender -- 6.7 Comparison to Standard Linear Moderation -- 6.8 Analysis Summary -- 6.9 Example SAS Code for Analyzing the Dental Measurement Data -- 6.9.1 Modeling Means in Child Age Assuming Constant Variances -- 6.9.2 Modeling Means and Variances in Child Age -- 6.9.3 Additive Models in Child Age and Child Gender -- 6.9.4 Moderation Models in Child Age and Child Gender -- 6.9.5 Example Output -- Reference -- Chapter 7: Example Analyses of the Epilepsy Seizure Rate Data -- 7.1 Choosing the Number of Folds and the Correlation Structure -- 7.2 Assessing Linearity of the Log of the Means in Visit -- 7.3 Comparison to Standard GEE Modeling -- 7.4 Modeling Means and Dispersions in Visit -- 7.5 Additive Models in Visit and Being in the Intervention Group -- 7.6 Adaptive Moderation of the Effect of Visit by Being in the Intervention Group -- 7.7 Comparison of Linear Additive and Moderation Models with Constant Dispersions -- 7.8 Direct Variance Modeling of Epilepsy Seizure Rates -- 7.9 Analysis Summary -- 7.10 Example SAS Code for Analyzing the Epilepsy Seizure Rate Data -- 7.10.1 Modeling Means in Visit Assuming Constant Dispersions -- 7.10.2 Modeling Means and Dispersions in Visit -- 7.10.3 Additive Models in Visit and Being in the Intervention Group -- 7.10.4 Moderation Models in Visit and Being in the Intervention Group -- 7.10.5 Direct Variance Modeling. 327 $a7.10.6 Example Output -- Reference -- Chapter 8: Example Analyses of the Dichotomous Respiratory Status Data -- 8.1 Choosing the Number of Folds and the Correlation Structure -- 8.2 Assessing Linearity of the Logits of the Means in Visit -- 8.3 Assessing Unit Versus Constant Dispersions -- 8.4 Comparison to Standard GEE Modeling -- 8.5 Modeling Means and Dispersions in Visit -- 8.6 Additive Models in Visit and Being on Active Treatment -- 8.7 Adaptive Moderation of the Effect of Visit by Being on Active Treatment -- 8.8 Comparison to Standard Linear Moderation -- 8.9 Direct Variance Modeling of Dichotomous Respiratory Status -- 8.10 Analysis Summary -- 8.11 Example SAS Code for Analyzing the Dichotomous Respiratory Status Data -- 8.11.1 Modeling Means in Visit Assuming Constant Dispersions -- 8.11.2 Modeling Means and Dispersions in Visit -- 8.11.3 Additive Models in Visit and Being on Active Treatment -- 8.11.4 Moderation Models in Visit and Being on Active Treatment -- 8.11.5 Direct Variance Modeling -- 8.11.6 Example Output -- Reference -- Chapter 9: Example Analyses of the Blood Lead Level Data -- 9.1 Choosing the Number of Folds and the Correlation Structure -- 9.2 Assessing Linearity of the Log of the Means in Week -- 9.3 Comparison to Standard GEE Modeling -- 9.4 Modeling Means and Dispersions in Week -- 9.5 Additive Models in Week and Being on Succimer -- 9.6 Adaptive Moderation of the Effect of Week by Being on Succimer -- 9.7 Direct Variance Modeling of Blood Lead Level Data -- 9.8 Analysis Summary -- 9.9 Example SAS Code for Analyzing the Blood Lead Level Data -- 9.9.1 Modeling Means in Week Assuming Constant Dispersions -- 9.9.2 Modeling Means and Dispersions in Week -- 9.9.3 Additive Models in Week and Being on Succimer -- 9.9.4 Moderation Models in Week and Being on Succimer -- 9.9.5 Direct Variance Modeling -- 9.9.6 Example Output. 327 $aReference -- Part II: Polytomous Outcomes -- Chapter 10: Multinomial Regression -- 10.1 Standard GEE Modeling -- 10.2 Partially and Fully Modified GEE Modeling -- 10.3 Alternate Correlation Structures -- 10.3.1 Independent Correlations -- 10.3.2 Exchangeable Correlations -- 10.3.3 Spatial Autoregressive Order 1 Correlations -- 10.3.4 Unstructured Correlations -- 10.3.5 Degeneracy in Correlation Estimates -- 10.4 Extended Linear Mixed Modeling -- 10.4.1 Estimating Equations for Means, Dispersions, and Correlations Based on the Likelihood -- 10.4.2 First Partial Derivatives with Respect to Mean Parameters -- 10.4.3 First Partial Derivatives with Respect to Correlation Parameters -- 10.4.4 Second Partial Derivatives with Respect to Mean Parameters -- 10.4.5 Second Partial Derivatives with Respect to Correlation Parameters -- 10.4.6 Second Partial Derivatives with Respect to Mean and Dispersion Parameters -- 10.4.7 Second Partial Derivatives with Respect to Mean and Correlation Parameters -- 10.4.8 Second Partial Derivatives with Respect to Dispersion and Correlation Parameters -- References -- Chapter 11: Ordinal Regression -- 11.1 Ordinal Regression Based on Individual Outcomes -- 11.1.1 Standard GEE Modeling -- 11.1.2 Partially and Fully Modified GEE Modeling -- 11.1.3 Alternate Correlation Structures -- 11.1.3.1 Independent Correlations -- 11.1.3.2 Exchangeable Correlations -- 11.1.3.3 Autoregressive Correlations -- 11.1.3.4 Unstructured Correlations -- 11.1.3.5 Degeneracy in Correlation Estimates -- 11.1.4 Extended Linear Mixed Modeling -- 11.1.4.1 Estimating Equations for Means, Dispersions, and Correlations Based on the Likelihood -- 11.1.4.2 First Partial Derivatives with Respect to Mean Parameters -- 11.1.4.3 First Partial Derivatives with Respect to Correlation Parameters -- 11.1.4.4 Second Partial Derivatives with Respect to Mean Parameters. 327 $a11.1.4.5 Second Partial Derivatives with Respect to Correlation Parameters. 330 $aThis book formulates methods for modeling continuous and categorical correlated outcomes that extend the commonly used methods: generalized estimating equations (GEE) and linear mixed modeling. Partially modified GEE adds estimating equations for variance/dispersion parameters to the standard GEE estimating equations for the mean parameters. Fully modified GEE provides alternate estimating equations for mean parameters as well as estimating equations for variance/dispersion parameters. The new estimating equations in these two cases are generated by maximizing a "likelihood" function related to the multivariate normal density function. Partially modified GEE and fully modified GEE use the standard GEE approach to estimate correlation parameters based on the residuals. Extended linear mixed modeling (ELMM) uses the likelihood function to estimate not only mean and variance/dispersion parameters, but also correlation parameters. Formulations are provided for gradient vectors and Hessianmatrices, for a multi-step algorithm for solving estimating equations, and model-based and robust empirical tests for assessing theory-based models. Standard GEE, partially modified GEE, fully modified GEE, and ELMM are demonstrated and compared using a variety of regression analyses of different types of correlated outcomes. Example analyses of correlated outcomes include linear regression for continuous outcomes, Poisson regression for count/rate outcomes, logistic regression for dichotomous outcomes, exponential regression for positive-valued continuous outcome, multinomial regression for general polytomous outcomes, ordinal regression for ordinal polytomous outcomes, and discrete regression for discrete numeric outcomes. These analyses also address nonlinearity in predictors based on adaptive search through alternative fractional polynomial models controlled by likelihood cross-validation (LCV) scores. Larger LCV scores indicate better models but not necessarilydistinctly better models. LCV ratio tests are used to identify distinctly better models. A SAS macro has been developed for analyzing correlated outcomes using standard GEE, partially modified GEE, fully modified GEE, and ELMM within alternative regression contexts. This macro and code for conducting the analyses addressed in the book are available online via the book?s Springer website. Detailed descriptions of how to use this macro and interpret its output are provided in the book. 606 $aStatistics 606 $aBiometry 606 $aStatistical Theory and Methods 606 $aBiostatistics 606 $aAnàlisi de regressió$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aBiometry. 615 14$aStatistical Theory and Methods. 615 24$aBiostatistics. 615 7$aAnàlisi de regressió 676 $a780 700 $aKnafl$b George J.$0755792 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910805583103321 996 $aModeling Correlated Outcomes Using Extensions of Generalized Estimating Equations and Linear Mixed Modeling$93882699 997 $aUNINA LEADER 05846nam 2200781Ia 450 001 9911020464903321 005 20200520144314.0 010 $a9786610411207 010 $a9781118856482 010 $a1118856481 010 $a9781118673447 010 $a1118673441 010 $a9781280411205 010 $a1280411201 010 $a9780470858837 010 $a0470858834 035 $a(CKB)1000000000239305 035 $a(EBL)255713 035 $a(OCoLC)76963099 035 $a(SSID)ssj0000155256 035 $a(PQKBManifestationID)11161047 035 $a(PQKBTitleCode)TC0000155256 035 $a(PQKBWorkID)10112749 035 $a(PQKB)10724061 035 $a(MiAaPQ)EBC255713 035 $a(Perlego)1014130 035 $a(EXLCZ)991000000000239305 100 $a20060116d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFinite difference methods in financial engineering $ea partial differential equation approach /$fDaniel J. Duffy 210 $aChichester, England ;$aHoboken, NJ $cJohn Wiley$dc2006 215 $a1 online resource (441 p.) 225 1 $aWiley finance series 300 $aDescription based upon print version of record. 311 08$a9780470858820 311 08$a0470858826 320 $aIncludes bibliographical references (p. [409]-416) and index. 327 $a0 Goals of this Book and Global Overview; Contents; 0.1 What is this Book?; 0.2 Why has this Book Been Written?; 0.3 For Whom is this Book Intended?; 0.4 Why Should I Read this Book?; 0.5 The Structure of this Book; 0.6 What this Book Does Not Cover; 0.7 Contact, Feedback and More Information; Part I The Continuous Theory Of Partial DifferentialI Equations; 1 An Introduction to Ordinary Differential Equations; 1.1 Introduction and Objectives; 1.2 Two-Point Boundary Value Problem; 1.2.1 Special Kinds of Boundary Condition; 1.3 Linear Boundary Value Problems; 1.4 Initial Value Problems 327 $a1.5 Some Special Cases1.6 Summary and Conclusions; 2 An Introduction to Partial Differential Equations; 2.1 Introduction and Objectives; 2.2 Partial Differential Equations; 2.3 Specialisations; 2.3.1 Elliptic Equations; 2.3.2 Free Boundary Value Problems; 2.4 Parabolic Partial Differential Equations; 2.4.1 Special Cases; 2.5 Hyperbolic Equations; 2.5.1 Second-Order Equations; 2.5.2 First-Order Equations; 2.6 Systems of Equations; 2.6.1 Parabolic Systems; 2.6.2 First-Order Hyperbolic Systems; 2.7 Equations Containing Integrals; 2.8 Summary and Conclusions 327 $a3 Second-Order Parabolic Differential Equations3.1 Introduction and Objectives; 3.2 Linear Parabolic Equations; 3.3 The Continuous Problem; 3.4 The Maximum Principle for Parabolic Equations; 3.5 A Special Case: One-Factor Generalised Black-Scholes Models; 3.6 Fundamental Solution and the Green's Function; 3.7 Integral Representation of the Solution of Parabolic PDEs; 3.8 Parabolic Equations in One Space Dimension; 3.9 Summary and Conclusions; 4 An Introduction to the Heat Equation in One Dimension; 4.1 Introduction and Objectives; 4.2 Motivation and Background 327 $a4.3 The Heat Equation and Financial Engineering4.4 The Separation of Variables Technique; 4.4.1 Heat Flow in a Road with Ends Held at Constant Temperature; 4.4.2 Heat Flow in a Rod Whose Ends are at a Specified Variable Temperature; 4.4.3 Heat Flow in an Infinite Rod; 4.4.4 Eigenfunction Expansions; 4.5 Transformation Techniques for the Heat Equation; 4.5.1 Laplace Transform; 4.5.2 Fourier Transform for the Heat Equation; 4.6 Summary and Conclusions; 5 An Introduction to the Method of Characteristics; 5.1 Introduction and Objectives; 5.2 First-Order Hyperbolic Equations; 5.2.1 An Example 327 $a5.3 Second-Order Hyperbolic Equations5.3.1 Numerical Integration Along the Characteristic Lines; 5.4 Applications to Financial Engineering; 5.4.1 Generalisations; 5.5 Systems of Equations; 5.5.1 An Example; 5.6 Propagation of Discontinuities; 5.6.1 Other Problems; 5.7 Summary and Conclusions; Part II FiniteI DifferenceI Methods: The Fundamentals; 6 An Introduction to the Finite Difference Method; 6.1 Introduction and Objectives; 6.2 Fundamentals of Numerical Differentiation; 6.3 Caveat: Accuracy and Round-Off Errors; 6.4 Where are Divided Differences Used in Instrument Pricing? 327 $a6.5 Initial Value Problems 330 $aThe world of quantitative finance (QF) is one of the fastest growing areas of research and its practical applications to derivatives pricing problem. Since the discovery of the famous Black-Scholes equation in the 1970's we have seen a surge in the number of models for a wide range of products such as plain and exotic options, interest rate derivatives, real options and many others. Gone are the days when it was possible to price these derivatives analytically. For most problems we must resort to some kind of approximate method. In this book we employ partial differential equations (PDE) to 410 0$aWiley finance series. 606 $aFinancial engineering$xMathematics 606 $aDerivative securities$xPrices$xMathematical models 606 $aFinite differences 606 $aDifferential equations, Partial$xNumerical solutions 615 0$aFinancial engineering$xMathematics. 615 0$aDerivative securities$xPrices$xMathematical models. 615 0$aFinite differences. 615 0$aDifferential equations, Partial$xNumerical solutions. 676 $a332.60151 686 $aQK 660$2rvk 686 $aSK 980$2rvk 700 $aDuffy$b Daniel J$0103056 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020464903321 996 $aFinite difference methods in financial engineering$94418772 997 $aUNINA