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Accounting and Statistical Analyses for Sustainable Development : Multiple Perspectives and Information-Theoretic Complexity Reduction
| Accounting and Statistical Analyses for Sustainable Development : Multiple Perspectives and Information-Theoretic Complexity Reduction |
| Autore | Lemke Claudia |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (288 pages) |
| Collana | Sustainable Management, Wertschöpfung und Effizienz |
| Soggetto topico | Environmental economics |
| Soggetto non controllato |
Environmental Economics
Sustainability Sustainable Development Goals (SDGs) Composite indicators Multilevel perspective Principal component analysis Information theory Open Access |
| ISBN | 3-658-33246-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Foreword -- Acknowledgement -- Table of contents -- List of abbreviations -- List of figures -- List of tables -- List of equations -- List of symbols -- Chapter 1 Introduction -- 1.1 Background and motivation -- 1.2 Research question and aim of the dissertation -- 1.3 Procedure -- Chapter 2 Conceptual framework of sustainable development -- 2.1 Definition of sustainable development and sustainability -- 2.2 The three contentual domains of sustainable development -- 2.2.1 Environmental protection -- 2.2.2 Social development -- 2.2.3 Economic prosperity -- 2.2.4 Integration of the three contentual domains -- 2.3 Stakeholders and change agents of sustainable development -- 2.3.1 The multilevel perspective -- 2.3.2 Corporate sustainability -- 2.3.3 Political goal setting: The United Nations's (UN) Sustainable Development Goals (SDGs) -- 2.3.4 Sustainability science -- 2.4 Summary -- Chapter 3 Measuring and assessing contributions to sustainable development -- 3.1 Principles of sustainable development measurement and assessment methods -- 3.2 Overview of quantitative sustainable development assessment methods -- 3.3 Sustainable development indicators -- 3.3.1 Corporate indicator frameworks -- 3.3.2 Meso-level indices -- 3.3.3 Macro-level indices -- 3.4 Summary -- Chapter 4 Methodology -- 4.1 Overview of sustainable development indices' calculation steps and methodological requirements -- 4.2 Methodological evaluation of sustainable development indices -- 4.3 Methodology of the Multilevel Sustainable Development Index (MLSDI) -- 4.3.1 Collection of sustainable development key figures -- 4.3.2 Preparation of sustainable development key figures -- 4.3.2.1 Meso-level transformation to macro-economic categories -- 4.3.2.2 Macro-level transformation of statistical classifications -- 4.3.3 Imputation of missing values.
4.3.3.1 Characterisation of missing values -- 4.3.3.2 Single time series imputation: Various methods depending on the missing data pattern -- 4.3.3.3 Multiple panel data imputation: Amelia II algorithm -- 4.3.3.4 Statistical tests of model assumptions -- 4.3.4 Standardisation to sustainable development key indicators -- 4.3.5 Outlier detection and treatment -- 4.3.5.1 Characterisation of outliers -- 4.3.5.2 Univariate Interquartile Range (IQR) method -- 4.3.6 Scaling -- 4.3.6.1 Characterisation of scales -- 4.3.6.2 Rescaling between ten and 100 -- 4.3.7 Weighting -- 4.3.7.1 Overview of weighting methods -- 4.3.7.2 Multivariate statistical analysis: Principal Component Analysis (PCA) -- 4.3.7.3 Multivariate statistical analysis: Partial Triadic Analysis (PTA) -- 4.3.7.4 Information theory: Maximum Relevance Minimum Redundancy Backward (MRMRB) algorithm -- 4.3.7.5 Statistical tests of model assumptions -- 4.3.8 Aggregation -- 4.3.9 Sensitivity analyses -- 4.4 Summary and interim conclusion -- Chapter 5 Empirical findings -- 5.1 Data base, objects of investigation, and time periods -- 5.2 Sustainable development key figures -- 5.2.1 Collection and preparation of sustainable development key figures -- 5.2.2 Imputation of missing values -- 5.3 Sustainable development key indicators -- 5.3.1 Alignment of the Global Reporting Initiative (GRI) and the Sustainable Development Goal (SDG) disclosures -- 5.3.1.1 Environmental sustainable development key indicators -- 5.3.1.2 Social sustainable development key indicators -- 5.3.1.3 Economic sustainable development key indicators -- 5.3.2 Summary statistics of the sustainable development growth indicators -- 5.3.3 Outlier detection and treatment -- 5.3.4 Empirical findings of the cleaned and rescaled sustainable development key indicators -- 5.3.4.1 Summary statistics. 5.3.4.2 Comparative analysis of the selected branches -- 5.4 Weighting -- 5.4.1 The Principal Component (PC) family's eigenvalues and explained cumulative variances -- 5.4.2 The Maximum Relevance Minimum Redundancy Backward (MRMRB) algorithm's discretisation and backward elimination -- 5.4.3 Comparative analysis of weights -- 5.4.4 Statistical tests of the Principal Component (PC) family -- 5.5 Empirical findings of the four composite sustainable development measures -- 5.5.1 Summary statistics -- 5.5.2 Comparative analysis of the selected branches -- 5.6 Sensitivity analyses -- 5.7 Summary -- Chapter 6 Discussion and conclusion -- 6.1 Implications for research -- 6.2 Implications for practice -- 6.3 Limitations and future outlook -- 6.4 Summary and conclusion -- Appendix -- A.1 Statistical classification scheme of economic activities in the European Union (EU) -- A.2 German health economy's statistical delimitation -- A.3 Statistical tests of sustainable development key figures -- A.4 Summary statistics of the sustainable development key indicators -- A.5 Outlier thresholds of the sustainable development key indicators -- A.6 Normality tests of z-score scaled sustainable development key indicators -- A.7 Sensitivities by the four composite sustainable development measures -- References. |
| Record Nr. | UNINA-9910473452403321 |
Lemke Claudia
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| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced Data Analysis in Neuroscience : Integrating Statistical and Computational Models / Daniel Durstewitz
| Advanced Data Analysis in Neuroscience : Integrating Statistical and Computational Models / Daniel Durstewitz |
| Autore | Durstewitz, Daniel |
| Pubbl/distr/stampa | Cham, : Springer, 2017 |
| Descrizione fisica | xxv, 292 p. : ill. ; 24 cm |
| Soggetto topico |
92C20 - Neural biology [MSC 2020]
62R07 - Statistical aspects of big data and data science [MSC 2020] 68T09 - Computational aspects of data analysis and big data [MSC 2020] |
| Soggetto non controllato |
Bootstrap methods
Change point analysis Clustering Dimensionality reduction Machine learning Multiple testing Multivariate maps and recurrent neural networks Multivariate statistics Neural time series Nonlinear dynamical systems Nonlinear oscillations Nonparametric time series modeling Principal component analysis Reconstructing state spaces from experimental data Statistical methods in neuroscience Statistical parameter estimation Unsupervised clustering |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0123545 |
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Durstewitz, Daniel
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| Cham, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Advanced Data Analysis in Neuroscience : Integrating Statistical and Computational Models / Daniel Durstewitz
| Advanced Data Analysis in Neuroscience : Integrating Statistical and Computational Models / Daniel Durstewitz |
| Autore | Durstewitz, Daniel |
| Pubbl/distr/stampa | Cham, : Springer, 2017 |
| Descrizione fisica | xxv, 292 p. : ill. ; 24 cm |
| Soggetto topico |
62R07 - Statistical aspects of big data and data science [MSC 2020]
68T09 - Computational aspects of data analysis and big data [MSC 2020] 92C20 - Neural biology [MSC 2020] |
| Soggetto non controllato |
Bootstrap methods
Change point analysis Clustering Dimensionality reduction Machine learning Multiple testing Multivariate maps and recurrent neural networks Multivariate statistics Neural time series Nonlinear dynamical systems Nonlinear oscillations Nonparametric time series modeling Principal component analysis Reconstructing state spaces from experimental data Statistical methods in neuroscience Statistical parameter estimation Unsupervised clustering |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00123545 |
|
Durstewitz, Daniel
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| Cham, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Chemometrics with R : Multivariate Data Analysis in the Natural and Life Sciences / Ron Wehrens
| Chemometrics with R : Multivariate Data Analysis in the Natural and Life Sciences / Ron Wehrens |
| Autore | Wehrens, Ron |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | Berlin, : Springer, 2020 |
| Descrizione fisica | xvi, 308 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62H25 - Factor analysis and principal components; correspondence analysis [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] |
| Soggetto non controllato |
Boootstrap
Clustering Linear regression Missing values Multidimensional Scaling Multivariate curve resolution Multivariate statistics Neural networks Non-Linear regression Partial least squares regression Principal component analysis R software Ssupport vector machines Statistical process control Time warping Variable Selection |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0248480 |
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Wehrens, Ron
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| Berlin, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Chemometrics with R : Multivariate Data Analysis in the Natural and Life Sciences / Ron Wehrens
| Chemometrics with R : Multivariate Data Analysis in the Natural and Life Sciences / Ron Wehrens |
| Autore | Wehrens, Ron |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | Berlin, : Springer, 2020 |
| Descrizione fisica | xvi, 308 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
62H25 - Factor analysis and principal components; correspondence analysis [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] 62R07 - Statistical aspects of big data and data science [MSC 2020] |
| Soggetto non controllato |
Boootstrap
Clustering Linear regression Missing values Multidimensional Scaling Multivariate curve resolution Multivariate statistics Neural networks Non-Linear regression Partial least squares regression Principal component analysis R software Ssupport vector machines Statistical process control Time warping Variable Selection |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00248480 |
|
Wehrens, Ron
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| Berlin, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
Coherence : In Signal Processing and Machine Learning / David Ramírez, Ignacio Santamaría, Louis Scharf
| Coherence : In Signal Processing and Machine Learning / David Ramírez, Ignacio Santamaría, Louis Scharf |
| Autore | Ramírez, David |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xxi, 487 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Santamaría, Ignacio
Scharf, Louis |
| Soggetto non controllato |
Beamforming and spectrum analysis
Canonical and multiset correlation analysis Coherence in compressed sensing Coherence in science and engineering Coherence in signal processing Correlation and partial correlation analysis Hypothesis testing for covariance structure Kernel methods Least squares and its applications Matched and adaptive subspace detectors Matrix optimization Multichannel coherence Multichannel detection of spacetime signals Multidimensional Scaling Normal and matrix distribution theory Passive and active detection Performance bounds and uncertainty quantification Principal component analysis Subspace averaging and its applications |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0276976 |
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Ramírez, David
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| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Coherence : In Signal Processing and Machine Learning / David Ramírez, Ignacio Santamaría, Louis Scharf
| Coherence : In Signal Processing and Machine Learning / David Ramírez, Ignacio Santamaría, Louis Scharf |
| Autore | Ramírez, David |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xxi, 487 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Santamaría, Ignacio
Scharf, Louis |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
68-XX - Computer science [MSC 2020] 94-XX - Information and communication theory, circuits [MSC 2020] |
| Soggetto non controllato |
Beamforming and spectrum analysis
Canonical and multiset correlation analysis Coherence in compressed sensing Coherence in science and engineering Coherence in signal processing Correlation and partial correlation analysis Hypothesis testing for covariance structure Kernel methods Least squares and its applications Matched and adaptive subspace detectors Matrix optimization Multichannel coherence Multichannel detection of spacetime signals Multidimensional Scaling Normal and matrix distribution theory Passive and active detection Performance bounds and uncertainty quantification Principal component analysis Subspace averaging and its applications |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00276976 |
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Ramírez, David
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| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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COMPSTAT : Proceedings in Computational Statistics, 9th Symposium held at Dubrovnik, Yugoslavia, 1990 / edited by Konstantin Momirović, Vesna Mildner
| COMPSTAT : Proceedings in Computational Statistics, 9th Symposium held at Dubrovnik, Yugoslavia, 1990 / edited by Konstantin Momirović, Vesna Mildner |
| Pubbl/distr/stampa | Heidelberg, : Physica-Verlag, 1990 |
| Descrizione fisica | xi, 336 p. : ill. ; 24 cm |
| Soggetto non controllato |
Classification
Data analysis Estimator Expert systems Factor analysis Fitting Generalized Linear Models Knowledge-based systems Likelihood Optimization Principal component analysis Statistical software Time series Variance |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00286706 |
| Heidelberg, : Physica-Verlag, 1990 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Environmental Data Analysis : An Introduction with Examples in R / Carsten Dormann
| Environmental Data Analysis : An Introduction with Examples in R / Carsten Dormann |
| Autore | Dormann, Carsten F. |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xix, 264 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
92D40 - Ecology [MSC 2020] |
| Soggetto non controllato |
ANOVA
Cluster analysis Data visualisation Design of experiments Environmetrics Generalized Linear Models Hypothesis Testing Maximum Likelihood Model selection Multiple regression Principal component analysis Regression |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0249064 |
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Dormann, Carsten F.
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| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Environmental Data Analysis : An Introduction with Examples in R / Carsten Dormann
| Environmental Data Analysis : An Introduction with Examples in R / Carsten Dormann |
| Autore | Dormann, Carsten F. |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | xix, 264 p. : ill. ; 24 cm |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
92D40 - Ecology [MSC 2020] |
| Soggetto non controllato |
ANOVA
Cluster analysis Data visualisation Design of experiments Environmetrics Generalized Linear Models Hypothesis Testing Maximum Likelihood Model selection Multiple regression Principal component analysis Regression |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00249064 |
|
Dormann, Carsten F.
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| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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