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Books on the topic 'R-parameter'

Author: Grafiati

Published: 6 September 2023

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1

Statistical tools for nonlinear regression: A practical guide with S-PLUS and R examples. 2nd ed. New York: Springer, 2004.

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2

Tucker, Gary B. pH. IN Williams, R. D.; Schuman, G. E., editors, Reclaiming mine soils and overburden in the western United States--Analytic parameters and procedures. S.l: s.n, 1987.

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3

Nash, John C. Nonlinear Parameter Optimization Using R Tools. Wiley & Sons, Incorporated, John, 2014.

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4

Nash, John C. Nonlinear Parameter Optimization Using R Tools. Wiley & Sons, Incorporated, John, 2014.

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5

Nash, John C. Nonlinear Parameter Optimization Using R Tools. Wiley & Sons, Incorporated, John, 2014.

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6

Nash, John C. Nonlinear Parameter Optimization Using R Tools. Wiley & Sons, Limited, John, 2014.

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7

Nonlinear Parameter Optimization Using R Tools. John Wiley & Sons Inc, 2014.

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8

Isermann, R. System Identification: Tutorials Presented at the 5th IFAC Symposium on Identification and System Parameter Estimation, F. R. Germany, September 1979. Elsevier Science & Technology Books, 2014.

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9

Loo, Colleen. TMS in the treatment of major depressive disorder. Edited by Charles M. Epstein, Eric M. Wassermann, and Ulf Ziemann. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780198568926.013.0040.

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There is a growing body of literature on animal experiments using behavioural paradigms or investigations of neurobiological mechanisms to assess the antidepressant effects of repetitive (r)TMS. This article summarizes the findings from these studies. The evidence from these studies is supportive of antidepressant effects for rTMS. This evidence has provided several plausible hypotheses for the antidepressant effects of rTMS. Several studies have suggested that rTMS may have neuroprotective effects. There are many variations in the way rTMS can be given as a clinical treatment, involving choices over treatment site, stimulation parameters and treatment course. Clinical trials to date have found rTMS to be safe when given within recommended parameter guidelines. Further research is needed to understand the role of rTMS with respect to other established antidepressant treatments.

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10

Zabrodin, Anton. Quantum spin chains and classical integrable systems. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0013.

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This chapter is a review of the recently established quantum-classical correspondence for integrable systems based on the construction of the master T-operator. For integrable inhomogeneous quantum spin chains with gl(N)-invariant R-matrices in finite-dimensional representations, the master T-operator is a sort of generating function for the family of commuting quantum transfer matrices depending on an infinite number of parameters. Any eigenvalue of the master T-operator is the tau-function of the classical modified KP hierarchy. It is a polynomial in the spectral parameter which is identified with the 0th time of the hierarchy. This implies a remarkable relation between the quantum spin chains and classical many-body integrable systems of particles of the Ruijsenaars-Schneider type. As an outcome, a system of algebraic equations can be obtained for the spectrum of the spin chain Hamiltonians.

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11

Huet, Sylvie, Anne Bouvier, Marie-Anne Poursat, and Emmanuel Jolivet. Statistical Tools for Nonlinear Regression: A Practical Guide with S-PLUS and R Examples (Springer Series in Statistics). Springer, 2003.

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12

Hankin, David, Michael S. Mohr, and Kenneth B. Newman. Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.001.0001.

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We present a rigorous but understandable introduction to the field of sampling theory for ecologists and natural resource scientists. Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and statistically sound inferences about the population as a whole. The inferences fall into two broad categories: (a) estimation of simple descriptive population parameters, such as means, totals, or proportions, for variables of interest, and (b) estimation of uncertainty associated with estimated parameter values. Although the targets of estimation are few and simple, estimates of means, totals, or proportions see important and often controversial uses in management of natural resources and in fundamental ecological research, but few ecologists or natural resource scientists have formal training in sampling theory. We emphasize the classical design-based approach to sampling in which variable values associated with units are regarded as fixed and uncertainty of estimation arises via various randomization strategies that may be used to select samples. In addition to covering standard topics such as simple random, systematic, cluster, unequal probability (stressing the generality of Horvitz–Thompson estimation), multi-stage, and multi-phase sampling, we also consider adaptive sampling, spatially balanced sampling, and sampling through time, three areas of special importance for ecologists and natural resource scientists. The text is directed to undergraduate seniors, graduate students, and practicing professionals. Problems emphasize application of the theory and R programming in ecological and natural resource settings.

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13

Egedi, Barbara, and Veronika Hegedűs, eds. Functional Heads Across Time. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780198871538.001.0001.

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Abstract This volume brings together studies that contribute to our knowledge about the role functional elements play in syntactic changes, and the semantic and functional features that are the driving force behind the changes. Parameter resettings, structural reanalyses, and changes in the feature specification of functional heads are explored related to the functional sequence of the clausal as well as the nominal and adpositional domains. The chapters in this book discuss ‘microdiachronic’ syntactic changes that often have implications for large-scale syntactic effects, such as word order variation and change, the emergence (and lexicalization) of syntactic projections, grammaticalization, and changes in information structural properties. The volume contains case studies of individual languages (English, German, Hungarian, Icelandic, Italian, Latin, Portuguese, and Romanian are included) as well as discussions of cross-linguistic phenomena. The studies heavily rely on digital corpora of historical or dialectal data. The chapters are organized in an order that essentially reflects the hierarchy of projections in the clausal functional sequence and the other distinguished ph(r)asal projections from CP to DP.

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14

Huet, Sylvie, Anne Bouvier, Marie-Anne Poursat, and Emmanuel Jolivet. Statistical Tools for Nonlinear Regression: A Practical Guide with S-PLUS and R Examples. Springer London, Limited, 2006.

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15

Essington, Timothy E. Introduction to Quantitative Ecology. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192843470.001.0001.

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Modern practice of ecology, conservation, and resource management demands unprecedented levels of quantitative proficiency in mathematical modeling and statistics. This text provides foundational training in the concepts and methods of mathematical and statistical modeling used in ecology, for readers with all levels of quantitative proficiency and confidence. The first chapter presents a generalized approach to develop ecological models and introduces the “describe, explain, and interpret” framework for linking the model world to the real world. Detailed treatment of population models illustrates the myriad ways in which one can develop a model, shows how modeling choices are informed by the ecological question at hand, and emphasizes the epistemology of quantitative techniques. The second part of the book illustrates how to estimate parameters of models from data, and how to use mathematical models combined with statistics to test hypotheses. The third part of the book is devoted to an in-depth development of technical skills to implement models in two common platforms: spreadsheets and the R programming language. The book concludes by demonstrating a quantitative approach to addressing a question that spans density-dependent versus density-independent population models, fitting models to data, evaluating the strength for density dependence using model selection, and evaluating the types of dynamic behaviors that the population might exhibit.

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