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Growth Curve Modeling Michael J. Panik

Growth Curve Modeling By Michael J. Panik

Growth Curve Modeling by Michael J. Panik


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Summary

Suitable for upper-undergraduate and graduate courses on growth modeling, this title presents an introduction to growth curve modeling and addresses how to monitor the change in variables over time since there is no one size fits all approach to growth measurement.

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Growth Curve Modeling Summary

Growth Curve Modeling: Theory and Applications by Michael J. Panik

Features recent trends and advances in the theory and techniques used to accurately measure and model growth Growth Curve Modeling: Theory and Applications features an accessible introduction to growth curve modeling and addresses how to monitor the change in variables over time since there is no one size fits all approach to growth measurement. A review of the requisite mathematics for growth modeling and the statistical techniques needed for estimating growth models are provided, and an overview of popular growth curves, such as linear, logarithmic, reciprocal, logistic, Gompertz, Weibull, negative exponential, and log-logistic, among others, is included. In addition, the book discusses key application areas including economic, plant, population, forest, and firm growth and is suitable as a resource for assessing recent growth modeling trends in the medical field. SAS(R) is utilized throughout to analyze and model growth curves, aiding readers in estimating specialized growth rates and curves. Including derivations of virtually all of the major growth curves and models, Growth Curve Modeling: Theory and Applications also features: Statistical distribution analysis as it pertains to growth modeling Trend estimations Dynamic site equations obtained from growth models Nonlinear regression Yield-density curves Nonlinear mixed effects models for repeated measurements data Growth Curve Modeling: Theory and Applications is an excellent resource for statisticians, public health analysts, biologists, botanists, economists, and demographers who require a modern review of statistical methods for modeling growth curves and analyzing longitudinal data. The book is also useful for upper-undergraduate and graduate courses on growth modeling.

Growth Curve Modeling Reviews

Thus, it is an excellent resource for statisticians, public health analysts, biologists, botanists, economists, and demographers who require a modern review of statistical methods for modeling growth curves and analyzing longitudinal data. (Zentralblatt MATH, 1 April 2015)

About Michael J. Panik

MICHAEL J. PANIK, PhD, is Professor Emeritus in the Department of Economics at the University of Hartford. He has served as a consultant to the Connecticut Department of Motor Vehicles as well as to a variety of healthcare organizations. In addition, Dr. Panik is the author of numerous books and journal articles in the areas of economics, mathematics, and applied econometrics.

Table of Contents

Preface xiii 1 Mathematical Preliminaries 1 1.1 Arithmetic Progression, 1 1.2 Geometric Progression, 2 1.3 The Binomial Formula, 4 1.4 The Calculus of Finite Differences, 5 1.5 The Number e, 9 1.6 The Natural Logarithm, 10 1.7 The Exponential Function, 11 1.8 Exponential and Logarithmic Functions: Another Look, 13 1.9 Change of Base of a Logarithm, 14 1.10 The Arithmetic (Natural) Scale versus the Logarithmic Scale, 15 1.11 Compound Interest Arithmetic, 17 2 Fundamentals of Growth 21 2.1 Time Series Data, 21 2.2 Relative and Average Rates of Change, 21 2.3 Annual Rates of Change, 25 2.4 Discrete versus Continuous Growth, 32 2.5 The Growth of a Variable Expressed in Terms of the Growth of its Individual Arguments, 36 2.6 Growth Rate Variability, 46 2.7 Growth in a Mixture of Variables, 47 3 Parametric Growth Curve Modeling 49 3.1 Introduction, 49 3.2 The Linear Growth Model, 50 3.3 The Logarithmic Reciprocal Model, 51 3.4 The Logistic Model, 52 3.5 The Gompertz Model, 54 3.6 The Weibull Model, 55 3.7 The Negative Exponential Model, 56 3.8 The von Bertalanffy Model, 57 3.9 The Log-Logistic Model, 59 3.10 The Brody Growth Model, 61 3.11 The Janoschek Growth Model, 62 3.12 The Lundqvist Korf Growth Model, 63 3.13 The Hossfeld Growth Model, 63 3.14 The Stannard Growth Model, 64 3.15 The Schnute Growth Model, 64 3.16 The Morgan Mercer Flodin (M M F) Growth Model, 66 3.17 The McDill Amateis Growth Model, 68 3.18 An Assortment of Additional Growth Models, 69 Appendix 3.A The Logistic Model Derived, 71 Appendix 3.B The Gompertz Model Derived, 74 Appendix 3.C The Negative Exponential Model Derived, 75 Appendix 3.D The von Bertalanffy and Richards Models Derived, 77 Appendix 3.E The Schnute Model Derived, 81 Appendix 3.F The McDill Amateis Model Derived, 83 Appendix 3.G The Sloboda Model Derived, 85 Appendix 3.H A Generalized Michaelis Menten Growth Equation, 86 4 Estimation of Trend 88 4.1 Linear Trend Equation, 88 4.2 Ordinary Least Squares (OLS) Estimation, 91 4.3 Maximum Likelihood (ML) Estimation, 92 4.4 The SAS System, 94 4.5 Changing the Unit of Time, 109 4.6 Autocorrelated Errors, 110 4.7 Polynomial Models in t, 126 4.8 Issues Involving Trended Data, 136 Appendix 4.A OLS Estimated and Related Growth Rates, 158 5 Dynamic Site Equations Obtained from Growth Models 164 5.1 Introduction, 164 5.2 Base-Age-Specific (BAS) Models, 164 5.3 Algebraic Difference Approach (ADA) Models, 166 5.4 Generalized Algebraic Difference Approach (GADA) Models, 169 5.5 A Site Equation Generating Function, 179 5.6 The Grounded GADA (g-GADA) Model, 184 Appendix 5.A Glossary of Selected Forestry Terms, 186 6 Nonlinear Regression 188 6.1 Intrinsic Linearity/Nonlinearity, 188 6.2 Estimation of Intrinsically Nonlinear Regression Models, 190 Appendix 6.A Gauss Newton Iteration Scheme: The Single Parameter Case, 214 Appendix 6.B Gauss Newton Iteration Scheme: The r Parameter Case, 217 Appendix 6.C The Newton Raphson and Scoring Methods, 220 Appendix 6.D The Levenberg Marquardt Modification/Compromise, 222 Appendix 6.E Selection of Initial Values, 223 7 Yield Density Curves 226 7.1 Introduction, 226 7.2 Structuring Yield Density Equations, 227 7.3 Reciprocal Yield Density Equations, 228 7.4 Weight of a Plant Part and Plant Density, 239 7.5 The Expolinear Growth Equation, 242 7.6 The Beta Growth Function, 249 7.7 Asymmetric Growth Equations (for Plant Parts), 253 Appendix 7.A Derivation of the Shinozaki and Kira Yield Density Curve, 257 Appendix 7.B Derivation of the Farazdaghi and Harris Yield Density Curve, 258 Appendix 7.C Derivation of the Bleasdale and Nelder Yield Density Curve, 259 Appendix 7.D Derivation of the Expolinear Growth Curve, 261 Appendix 7.E Derivation of the Beta Growth Function, 263 Appendix 7.F Derivation of Asymmetric Growth Equations, 266 Appendix 7.G Chanter Growth Function, 269 8 Nonlinear Mixed-Effects Models for Repeated Measurements Data 270 8.1 Some Basic Terminology Concerning Experimental Design, 270 8.2 Model Specification, 271 8.3 Some Special Cases of the Hierarchical Global Model, 274 8.4 The SAS/STAT NLMIXED Procedure for Fitting Nonlinear Mixed-Effects Model, 276 9 Modeling the Size and Growth Rate Distributions of Firms 293 9.1 Introduction, 293 9.2 Measuring Firm Size and Growth, 294 9.3 Modeling the Size Distribution of Firms, 294 9.4 Gibrat s Law (GL), 297 9.5 Rationalizing the Pareto Firm Size Distribution, 299 9.6 Modeling the Growth Rate Distribution of Firms, 300 9.7 Basic Empirics of Gibrat s Law (GL), 305 9.8 Conclusion, 313 Appendix 9.A Kernel Density Estimation, 314 Appendix 9.B The Log-Normal and Gibrat Distributions, 322 Appendix 9.C The Theory of Proportionate Effect, 326 Appendix 9.D Classical Laplace Distribution, 328 Appendix 9.E Power-Law Behavior, 332 Appendix 9.F The Yule Distribution, 338 Appendix 9.G Overcoming Sample Selection Bias, 339 10 Fundamentals of Population Dynamics 352 10.1 The Concept of a Population, 352 10.2 The Concept of Population Growth, 353 10.3 Modeling Population Growth, 354 10.4 Exponential (Density-Independent) Population Growth, 357 10.5 Density-Dependent Population Growth, 363 10.6 Beverton Holt Model, 371 10.7 Ricker Model, 374 10.8 Hassell Model, 377 10.9 Generalized Beverton Holt (B H) Model, 380 10.10 Generalized Ricker Model, 382 Appendix 10.A A Glossary of Selected Population Demography/Ecology Terms, 389 Appendix 10.B Equilibrium and Stability Analysis, 391 Appendix 10.C Discretization of the Continuous-Time Logistic Growth Equation, 400 Appendix 10.D Derivation of the B H S R Relationship, 401 Appendix 10.E Derivation of the Ricker S R Relationship, 403 Appendix A 405 References 420 Index 431

Additional information

CIN1118764048G
9781118764046
1118764048
Growth Curve Modeling: Theory and Applications by Michael J. Panik
Used - Good
Hardback
John Wiley & Sons Inc
20140221
454
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in good condition, but if you are not entirely satisfied please get in touch with us

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