| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNISA996465370503316 |
|
|
Autore |
Ghezzi Carlo |
|
|
Titolo |
Being a Researcher [[electronic resource] ] : An Informatics Perspective / / by Carlo Ghezzi |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2020.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (143 pages) : illustrations |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Computers |
Ethics |
Education—Data processing |
The Computing Profession |
Computers and Education |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references. |
|
|
|
|
|
|
Nota di contenuto |
|
1What is research and why we do it -- 2Research Methodology -- 3The products of research: publication and beyond -- 4The researcher's progress -- 5Research evaluation -- 6Research ethics. |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
This book explores research from the researchers’ perspective: why to engage in research, what methods to follow, how to operate in daily life, what the responsibilities are, how to engage with society, and the ethical issues confronting professionals in their day-to-day research. The book systematically discusses what every student should be told when entering academic or industrial research so that they can avoid going through the painful process of learning by personal experience and lots of errors. Rather than being technical, it is philosophical and sometimes even anecdotal, combining factual information and commonly accepted knowledge on research and its methods, while at the same time clearly distinguishing between objective and factual concepts and data, and subjective considerations. The book is about scientific research in general and as such holds true for any scientific field. However, it is fair to say that the different fields differ in their research cultures and in their eco-systems. The book reflects the |
|
|
|
|
|
|
|
|
|
|
|
|
|
author’s experience accumulated over almost 50 years of teaching graduate courses and lecturing in doctoral symposia at Politecnico di Milano, University of Zurich, TU Wien, Peking University, and at various conferences, and of academic research in informatics (also known as computer science). This book is mainly intended for students who are considering research as a possible career option; for in-progress researchers who have entered doctoral programs; and for junior postdoctoral researchers. It will also appeal to senior researchers involved in mentoring students and junior researchers. . |
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910141572603321 |
|
|
Autore |
Pourahmadi Mohsen |
|
|
Titolo |
High-dimensional covariance estimation [[electronic resource] /] / Mohsen Pourahmadi |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Hoboken, NJ, : Wiley, c2013 |
|
|
|
|
|
|
|
ISBN |
|
1-118-57366-8 |
1-118-57361-7 |
1-118-57365-X |
|
|
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (208 p.) |
|
|
|
|
|
|
Collana |
|
Wiley series in probability and statistics |
|
|
|
|
|
|
Classificazione |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Analysis of covariance |
Multivariate analysis |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Description based upon print version of record. |
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and index. |
|
|
|
|
|
|
Nota di contenuto |
|
HIGH-DIMENSIONAL COVARIANCE ESTIMATION; CONTENTS; PREFACE; I MOTIVATION AND THE BASICS; 1 INTRODUCTION; 1.1 Least Squares and Regularized Regression; 1.2 Lasso: Survival of the Bigger; 1.3 Thresholding the Sample Covariance Matrix; 1.4 Sparse PCA and Regression; 1.5 Graphical Models: Nodewise Regression; 1.6 Cholesky Decomposition and Regression; 1.7 The Bigger Picture: Latent Factor Models; 1.8 Further Reading; 2 DATA, SPARSITY, AND REGULARIZATION; 2.1 Data Matrix: Examples; 2.2 Shrinking the Sample Covariance Matrix; 2.3 Distribution of the Sample Eigenvalues |
|
|
|
|
|
|
|
|
|
|
|
2.4 Regularizing Covariances Like a Mean2.5 The Lasso Regression; 2.6 Lasso: Variable Selection and Prediction; 2.7 Lasso: Degrees of Freedom and BIC; 2.8 Some Alternatives to the Lasso Penalty; 3 COVARIANCE MATRICES; 3.1 Definition and Basic Properties; 3.2 The Spectral Decomposition; 3.3 Structured Covariance Matrices; 3.4 Functions of a Covariance Matrix; 3.5 PCA: The Maximum Variance Property; 3.6 Modified Cholesky Decomposition; 3.7 Latent Factor Models; 3.8 GLM for Covariance Matrices; 3.9 GLM via the Cholesky Decomposition; 3.10 GLM for Incomplete Longitudinal Data |
3.10.1 The Incoherency Problem in Incomplete Longitudinal Data3.10.2 The Incomplete Data and The EM Algorithm; 3.11 A Data Example: Fruit Fly Mortality Rate; 3.12 Simulating Random Correlation Matrices; 3.13 Bayesian Analysis of Covariance Matrices; II COVARIANCE ESTIMATION: REGULARIZATION; 4 REGULARIZING THE EIGENSTRUCTURE; 4.1 Shrinking the Eigenvalues; 4.2 Regularizing The Eigenvectors; 4.3 A Duality between PCA and SVD; 4.4 Implementing Sparse PCA: A Data Example; 4.5 Sparse Singular Value Decomposition (SSVD); 4.6 Consistency of PCA; 4.7 Principal Subspace Estimation; 4.8 Further Reading |
5 SPARSE GAUSSIAN GRAPHICAL MODELS5.1 Covariance Selection Models: Two Examples; 5.2 Regression Interpretation of Entries of Σ-1; 5.3 Penalized Likelihood and Graphical Lasso; 5.4 Penalized Quasi-Likelihood Formulation; 5.5 Penalizing the Cholesky Factor; 5.6 Consistency and Sparsistency; 5.7 Joint Graphical Models; 5.8 Further Reading; 6 BANDING, TAPERING, AND THRESHOLDING; 6.1 Banding the Sample Covariance Matrix; 6.2 Tapering the Sample Covariance Matrix; 6.3 Thresholding the Sample Covariance Matrix; 6.4 Low-Rank Plus Sparse Covariance Matrices; 6.5 Further Reading |
7 MULTIVARIATE REGRESSION: ACCOUNTING FOR CORRELATION7.1 Multivariate Regression and LS Estimators; 7.2 Reduced Rank Regressions (RRR); 7.3 Regularized Estimation of B; 7.4 Joint Regularization of (B, Ω); 7.5 Implementing MRCE: Data Examples; 7.5.1 Intraday Electricity Prices; 7.5.2 Predicting Asset Returns; 7.6 Further Reading; BIBLIOGRAPHY; INDEX; WILEY SERIES IN PROBABILITY AND STATISTICS |
|
|
|
|
|
|
Sommario/riassunto |
|
"Focusing on methodology and computation more than on theorems and proofs, this book provides computationally feasible and statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional data. Extensive in breadth and scope, it features ample applications to a number of applied areas, including business and economics, computer science, engineering, and financial mathematics; recognizes the important and significant contributions of longitudinal and spatial data; and includes various computer codes in R throughout the text and on an author-maintained web site"-- |
"The aim of this book is to provide computationally feasible and statistically efficient methods for estimating sparse and large covariance matrices of high-dimensional data"-- |
|
|
|
|
|
|
|
| |