Spectral analysis and time series / M.B. Priestley.

Por: Priestley, M. B. (Maurice Bertram)Series Probability and mathematical statisticsEditor: London ; New York : Academic Press, 1981Descripción: 2 v. (xvii, [45], 890 p.) : ill. ; 24 cmISBN: 0125649010 (v. 1); 0125649029 (v. 2)Tema(s): Time-series analysis | Spectral theory (Mathematics)Otra clasificación: *CODIGO*
Contenidos:
v. 1. Univariate series.-- 

Contents
Preface vii
Preface to Volume 2 ix
Contents of Volume 2 xvi
List of Main Notation xviii
Volume [1]
Chapter 1. Basic Concepts [1]
1.1. The Nature of Spectral Analysis [1]
1.2. Types of Processes [2]
1.3. Periodic and Non-periodic Functions [3]
1.4. Generalized Harmonic Analysis [7]
1.5. Energy Distribution [8]
1.6. Random Processes [10]
1.7. Stationary Random Processes [14]
1.8. Spectral Analysis of Stationary Random Processes [15]
1.9. Spectral Analysis of Non-stationary Random
Processes [17]
1.10. Time Series Analysis: Use of Spectral Analysis in Practice [18]
Chapter 2. Elements of Probability Theory [28]
2.1. Introduction [28]
2.2. Some Terminology [28]
2.3. Definition of Probability [30]
2.3.1. Axiomatic approach to probability [31]
2.3.2. The classical definition [33]
2.3.3. Probability spaces [34]
2.4. Conditional Probability and Independence [34]
2.4.1. Independent events [36]
2.5. Random Variables [37]
2.5.1. Defining probabilities for random variables [38]
2.6. Distribution Functions [39]
2.6.1. Decomposition of distribution functions [40]
2.7. Discrete, Continuous and Mixed Distributions [41]
2.8. Means, Variances and Moments [47]
2.8.1. Chebyshev’s inequality [57]
2.9. The Expectation Operator [52]
2.9.1. General transformations [53]
2.10. Generating Functions [55]
2.10.1. Probability generating functions [55]
2.10.2. Moment generating functions [55]
2.10.3. Characteristic functions [57]
2.10.4. Cumulant generating functions [58]
2.11. Some Special Distributions [59]
2.12. Bivariate Distributions [67]
2.12.1. Bivariate cumulative distribution functions [68]
2.12.2. Marginal distributions [69]
2.12.3. Conditional distributions [70]
2.12.4. Independent random variables [71]
2.12.5. Expectation for bivariate distributions [72]
2.12.6. Conditional expectation [73]
2.12.7. Bivariate moments [77]
2.12.8. Bivariate moment generating functions and characteristic functions [82]
2.12.9. Bivariate normal distribution [84]
2.12.10. Transformations of variables [86]
2.13. Multivariate Distributions [87]
2.13.1. Mean and variance of linear combinations [89]
2.13.2. Multivariate normal distribution [90]
2.14. The Law of Large Numbers and the Central Limit Theorem [92]
2.14.1. The law of large numbers [92]
2.14.2. Application to the binomial distribution [94]
2.14.3. The central limit theorem [95]
2.14.4. Properties of means and variances of random samples [96]
Chapter 3. Stationary Random Processes
3.1. Probablistic Description of Random Processes [100]
3.1.1. Realizations and ensembles [101]
3.1.2. Specification of a random process [101]
3.2. Stationary Processes [104]
3.3. The Autocovariance and Autocorrelation Functions [106]
3.3.1. General properties of R(r) and p(r) [108]
3.3.2. Positive semi-definite property of R(t) and p(r) [109]
3.3.3. Complex valued processes [110]
3.3.4. Use of the autocorrelation function process in practice [111]
3.4. Stationary and Evolutionary Processes [112]
3.4.1. Gaussian (normal) processes [113]
3.5. Special Discrete Parameter Models [114]
3.5.1.Purely random processes: “white noise” [114]
3.5.2.First order autoregressive processes (linear Markov processes) [116]
3.5.3.Second order autoregressive processes [123]
3.5.4.Autoregressive processes of general order [132]
3.5.5.Moving average processes [135]
3.5.6.Mixed autoregressive/moving average processes [138]
3.5.7.The general linear process [141]
3.5.8.Harmonic processes [147]
Stochastic Limiting Operations [150]
3.6.1.Stochastic continuity [151]
3:6.2.Stochastic differentiability [153]
3.6.3.Integration [154]
3.6.4.Interpretation of the derivatives of the autocorrelation function [155]
3.7 Standard Continuous Parameter Models [156]
3.7.1.Purely random processes (continuous “white noise”) [156]
3.7.2.First order autoregressive processes [158]
3.7.3.Examples of first order autoregressive processes [167]
3.7.4.Second order autoregressive processes [169]
3.7.5.Autogressive processes of general order [174]
3.7.6.Moving average processes [174]
3.7.7.Mixed autoregressive/moving average processes [176]
3.7.8.General linear processes [177]
3.7.9.Harmonic processes [179]
3.7.10.Filtered Poisson process, shot noise and Campbell’s theorem [179]
Chapter 4. Spectral Analysis [184]
4.1.Introduction [184]
4.2.Fourier Series for Functions with Periodicity 2 [184]
4.2.1. The L2 theory for Fourier series [189]
4.2.2. Geometrical interpretation of Fourier series Hilbert space [190]
4.3.Fourier Series for Functions of General Periodicity [194]
4.4.Spectral Analysis of Periodic Functions [194]
4.4.1. Functions of general periodicity [197]
4.5.Non-periodic Functions: Fourier Integral [198]
4.5.1. Nature of conditions for the existence of Fourier series and Fourier integrals [202]
4.6.Spectral Analysis of Non-periodic Functions [204]
4.7.Spectral Analysis of Stationary Processes [206]
4.8.Relationship between the Spectral Density Function and the Autocovariance and Autocorrelation Functions [210]
4.8.1. Normalized power spectra [215]
4.8.2. The Wiener-Khintchine theorem [218]
4.8.3. Discrete parameter processes [222]
4.9. Decomposition of the Integrated Spectrum [226]
4.10. Examples of Spectfa for some Simple Models [233]
4.11. Spectral Representation of Stationary Processes [243]
4.12. Linear Transformations and Filters [263]
4.12.1. Filter terminology: gain and phase [270]
4.12.2. Operational forms of filter relationships: calculation of spectra [276]
4.12.3. Transformation of the autocovariance function [280]
4.12.4. Processes with rational spectra [283]
4.12.5. Axiomatic treatment of filters [285]
Chapter 5. Estimation in the Time Domain [291]
5.1. Time Series Analysis [291]
5.2. Basic Ideas of Statistical Inference [292]
5.2.1. Point estimation [295]
5.2.2. General methods of estimation [304]
5.3. Estimation of Autocovariance and Autocorrelation Functions [317]
5.3.1. Form of the data [317]
5.3.2. Estimation of the mean [318]
5.3.3. Estimation of the autocovariance function [321]
5.3.4. Estimation of the'autocorrelation function [330]
5.3.5. Asymptotic distribution of the sample mean autocovariances and autocorrelations [337]
5.3.6. The ergodic property [340]
5.3.7. Continuous parameter processes [343]
5.4. Estimation of Parameters in Standard Models [345]
5.4.1. Estimation of parameters in autoregressive models [346]
5.4.2. Estimation of parameters in moving average models [354]
5.4.3. Estimation of parameters in mixed ARMA models [359]
5.4.4. Confidence intervals for the parameters [364]
5.4.5. Determining the order of the model [370]
5.4.6. Continuous parameter models [380]
5.5. Analysis of the Canadian Lynx Data [384]
Chapter 6. Estimation in the Frequency Domain [389]
6.1. Discrete spectra [390]
6.1.1. Estimation [391]
6.1.2. Periodgram analysis [394]
6.1.3. Sampling properties of the periodogram [397]
6.1.4. Tests for periodogram ordinates [406]
6.2. Continuous Spectra [415]
6.2.1. Finite Fourier transforms [418]
6.2.2. Properties of the periodogram of a linear process [420]
6.2.3. Consistent estimates of the spectral density function: spectral windows [432]
6.2.4. Sampling properties of spectral estimates [449]
6.2.5. Estimation of the integrated spectrum [471]
6.2.6. Goodness-of-flt tests [475]
6.2.7. Continuous parameter processes [494]
6.3. Mixed Spectra [499]
Chapter 7. Spectral Analysis in Practice [502]
7.1. Setting up a Spectral Analysis [502]
7.1.1. The aliasing effect [504]
7.2. Measures of Precision of Spectral Estimates [570]
7.3. Resolvability and Bandwidth [513]
7.3.1. Role of spectral bandwidth [513]
7.3.2. Role of window bandwidth [517]
7.4. Design Relations for Spectral Estimation: Choice of Window Parameters, Record Length and Frequency Interval [528]
7.4.1. Prewhitening and tapering [556]
7.5. Choice of Window [563]
7.6. Computation of Spectral Estimates: The Fast Fourier Transform [575]
7.7. Trend Removal and Seasonal Adjustment: Regression Analysis and Digital Filters [537]
7.8. Autoregressive, ARMA, and Maximum Entropy Spectral Estimation; CAT Criterion [600]
7.9. Some Examples [607]
Chapter 8. Analysis of Processes with Mixed Spectra [613]
8.1. Nature of the Problem [613]
8.2. Types of Models [614]
8.3. Tests Based on the Periodogram [616]
8.4. The P(A) Test [626]
8.5. Analysis of Simulated Series [642]
8.6. Estimation of the Spectral Density Function [648]
Appendix Ai
v. 2. Multivariate series, prediction and control.

Volume [2]
Chapter 9. Multivariate and Multidimensional Processes [654]
9.1. Correlation and Spectral Properties of Multivariate Stationary Processes [655]
9.2. Linear Relationships [669]
9.2.1. Linear relationships with added noise [671]
9.2.2. The Box-Jenkins “transfer function” model [676]
9.3. Multiple and Partial Coherency [681]
9.4. Multivariate AR, MA, and ARMA Models [685]
9.5. Estimation of Cross-spectra [692]
9.5.1. Estimation of co-spectra and quadrature spectra [701]
9.5.2. Estimation of cross-amplitude and phase spectra [702]
9.5.3. Estimation of coherency
9.5.4. Practical considerations: alignment techniques [706]
9.6. Numerical Examples [712]
9.7. Correlation and Spectral Properties of Multidimensional Processes [718]
9.7.1. Two-dimensional mixed spectra [724]
Chapter 10. Prediction, Filtering and Control [727]
10.1. The Prediction Problem [722]
10.1.1. Spectral factorization and linear representations [730]
10.1.2. Geometric representation of linear prediction [736]
10.1.3. The Kolmogorov approach [738]
10.1.4. The Wiener approach [748]
10.1.5. The Wold decomposition [755]
10.1.6. Prediction for multivariate processes [760]
10.2. The Box-Jenkins Approach to Forecasting [762]
10.3. The Filtering Problem [773]
10.3.1. The Wiener filter [775]
10.4. Linear Control Systems [780]
10.4.1. Minimum variance control [781]
10.4.2. System identification [786]
10.4.3. Multivariate systems [791]
10.4.4. State space representations [797]
10.5. Multivariate Time Series Model Fitting [800]
10.5.1. Identifiability of multivariate ARMA models [801]
10.5.2. Fitting state space models via canonical correlations analysis [804]
10.6. State Space Approach to Forecasting and Filtering Problems: Kalman Filtering [807]
Chapter 11. Non-stationarity and Non-linearity [816]
11.1. Spectral Analysis of Non-stationary Processes: Basic Considerations [817]
11.2. The Theory of Evolutionary Spectra [821]
11.2.1. Estimation of evolutionary spectra [837]
11.2.2. Complex demodulation [848]
11.3. Some Other Definitions of Time-dependent “Spectra” [855]
11.4. Prediction, Filtering and Control for Non-stationary Processes [859]
11.5. General Non-linear Models [866]
11.5.1. Volterra expansions and polyspectra [869]
11.6. Some Special Non-linear Models: Bilinear, Threshold, and Exponential Models [877]
References Ri
Author Index li
Subject Index Ivii
List(s) this item appears in: Últimas adquisiciones
    Average rating: 0.0 (0 votes)
Item type Home library Shelving location Call number Materials specified Status Date due Barcode
Libros Libros Instituto de Matemática, CONICET-UNS
Últimas adquisiciones 62 P949 (Browse shelf) v.1 y v. 2 Available A-9346

Includes bibliographies and indexes.

v. 1. Univariate series.--

Contents --
Preface vii --
Preface to Volume 2 ix --
Contents of Volume 2 xvi --
List of Main Notation xviii --
Volume [1] --
Chapter 1. Basic Concepts [1] --
1.1. The Nature of Spectral Analysis [1] --
1.2. Types of Processes [2] --
1.3. Periodic and Non-periodic Functions [3] --
1.4. Generalized Harmonic Analysis [7] --
1.5. Energy Distribution [8] --
1.6. Random Processes [10] --
1.7. Stationary Random Processes [14] --
1.8. Spectral Analysis of Stationary Random Processes [15] --
1.9. Spectral Analysis of Non-stationary Random --
Processes [17] --
1.10. Time Series Analysis: Use of Spectral Analysis in Practice [18] --
Chapter 2. Elements of Probability Theory [28] --
2.1. Introduction [28] --
2.2. Some Terminology [28] --
2.3. Definition of Probability [30] --
2.3.1. Axiomatic approach to probability [31] --
2.3.2. The classical definition [33] --
2.3.3. Probability spaces [34] --
2.4. Conditional Probability and Independence [34] --
2.4.1. Independent events [36] --
2.5. Random Variables [37] --
2.5.1. Defining probabilities for random variables [38] --
2.6. Distribution Functions [39] --
2.6.1. Decomposition of distribution functions [40] --
2.7. Discrete, Continuous and Mixed Distributions [41] --
2.8. Means, Variances and Moments [47] --
2.8.1. Chebyshev’s inequality [57] --
2.9. The Expectation Operator [52] --
2.9.1. General transformations [53] --
2.10. Generating Functions [55] --
2.10.1. Probability generating functions [55] --
2.10.2. Moment generating functions [55] --
2.10.3. Characteristic functions [57] --
2.10.4. Cumulant generating functions [58] --
2.11. Some Special Distributions [59] --
2.12. Bivariate Distributions [67] --
2.12.1. Bivariate cumulative distribution functions [68] --
2.12.2. Marginal distributions [69] --
2.12.3. Conditional distributions [70] --
2.12.4. Independent random variables [71] --
2.12.5. Expectation for bivariate distributions [72] --
2.12.6. Conditional expectation [73] --
2.12.7. Bivariate moments [77] --
2.12.8. Bivariate moment generating functions and characteristic functions [82] --
2.12.9. Bivariate normal distribution [84] --
2.12.10. Transformations of variables [86] --
2.13. Multivariate Distributions [87] --
2.13.1. Mean and variance of linear combinations [89] --
2.13.2. Multivariate normal distribution [90] --
2.14. The Law of Large Numbers and the Central Limit Theorem [92] --
2.14.1. The law of large numbers [92] --
2.14.2. Application to the binomial distribution [94] --
2.14.3. The central limit theorem [95] --
2.14.4. Properties of means and variances of random samples [96] --
Chapter 3. Stationary Random Processes --
3.1. Probablistic Description of Random Processes [100] --
3.1.1. Realizations and ensembles [101] --
3.1.2. Specification of a random process [101] --
3.2. Stationary Processes [104] --
3.3. The Autocovariance and Autocorrelation Functions [106] --
3.3.1. General properties of R(r) and p(r) [108] --
3.3.2. Positive semi-definite property of R(t) and p(r) [109] --
3.3.3. Complex valued processes [110] --
3.3.4. Use of the autocorrelation function process in practice [111] --
3.4. Stationary and Evolutionary Processes [112] --
3.4.1. Gaussian (normal) processes [113] --
3.5. Special Discrete Parameter Models [114] --
3.5.1.Purely random processes: “white noise” [114] --
3.5.2.First order autoregressive processes (linear Markov processes) [116] --
3.5.3.Second order autoregressive processes [123] --
3.5.4.Autoregressive processes of general order [132] --
3.5.5.Moving average processes [135] --
3.5.6.Mixed autoregressive/moving average processes [138] --
3.5.7.The general linear process [141] --
3.5.8.Harmonic processes [147] --
Stochastic Limiting Operations [150] --
3.6.1.Stochastic continuity [151] --
3:6.2.Stochastic differentiability [153] --
3.6.3.Integration [154] --
3.6.4.Interpretation of the derivatives of the autocorrelation function [155] --
3.7 Standard Continuous Parameter Models [156] --
3.7.1.Purely random processes (continuous “white noise”) [156] --
3.7.2.First order autoregressive processes [158] --
3.7.3.Examples of first order autoregressive processes [167] --
3.7.4.Second order autoregressive processes [169] --
3.7.5.Autogressive processes of general order [174] --
3.7.6.Moving average processes [174] --
3.7.7.Mixed autoregressive/moving average processes [176] --
3.7.8.General linear processes [177] --
3.7.9.Harmonic processes [179] --
3.7.10.Filtered Poisson process, shot noise and Campbell’s theorem [179] --
Chapter 4. Spectral Analysis [184] --
4.1.Introduction [184] --
4.2.Fourier Series for Functions with Periodicity 2 [184] --
4.2.1. The L2 theory for Fourier series [189] --
4.2.2. Geometrical interpretation of Fourier series Hilbert space [190] --
4.3.Fourier Series for Functions of General Periodicity [194] --
4.4.Spectral Analysis of Periodic Functions [194] --
4.4.1. Functions of general periodicity [197] --
4.5.Non-periodic Functions: Fourier Integral [198] --
4.5.1. Nature of conditions for the existence of Fourier series and Fourier integrals [202] --
4.6.Spectral Analysis of Non-periodic Functions [204] --
4.7.Spectral Analysis of Stationary Processes [206] --
4.8.Relationship between the Spectral Density Function and the Autocovariance and Autocorrelation Functions [210] --
4.8.1. Normalized power spectra [215] --
4.8.2. The Wiener-Khintchine theorem [218] --
4.8.3. Discrete parameter processes [222] --
4.9. Decomposition of the Integrated Spectrum [226] --
4.10. Examples of Spectfa for some Simple Models [233] --
4.11. Spectral Representation of Stationary Processes [243] --
4.12. Linear Transformations and Filters [263] --
4.12.1. Filter terminology: gain and phase [270] --
4.12.2. Operational forms of filter relationships: calculation of spectra [276] --
4.12.3. Transformation of the autocovariance function [280] --
4.12.4. Processes with rational spectra [283] --
4.12.5. Axiomatic treatment of filters [285] --
Chapter 5. Estimation in the Time Domain [291] --
5.1. Time Series Analysis [291] --
5.2. Basic Ideas of Statistical Inference [292] --
5.2.1. Point estimation [295] --
5.2.2. General methods of estimation [304] --
5.3. Estimation of Autocovariance and Autocorrelation Functions [317] --
5.3.1. Form of the data [317] --
5.3.2. Estimation of the mean [318] --
5.3.3. Estimation of the autocovariance function [321] --
5.3.4. Estimation of the'autocorrelation function [330] --
5.3.5. Asymptotic distribution of the sample mean autocovariances and autocorrelations [337] --
5.3.6. The ergodic property [340] --
5.3.7. Continuous parameter processes [343] --
5.4. Estimation of Parameters in Standard Models [345] --
5.4.1. Estimation of parameters in autoregressive models [346] --
5.4.2. Estimation of parameters in moving average models [354] --
5.4.3. Estimation of parameters in mixed ARMA models [359] --
5.4.4. Confidence intervals for the parameters [364] --
5.4.5. Determining the order of the model [370] --
5.4.6. Continuous parameter models [380] --
5.5. Analysis of the Canadian Lynx Data [384] --
Chapter 6. Estimation in the Frequency Domain [389] --
6.1. Discrete spectra [390] --
6.1.1. Estimation [391] --
6.1.2. Periodgram analysis [394] --
6.1.3. Sampling properties of the periodogram [397] --
6.1.4. Tests for periodogram ordinates [406] --
6.2. Continuous Spectra [415] --
6.2.1. Finite Fourier transforms [418] --
6.2.2. Properties of the periodogram of a linear process [420] --
6.2.3. Consistent estimates of the spectral density function: spectral windows [432] --
6.2.4. Sampling properties of spectral estimates [449] --
6.2.5. Estimation of the integrated spectrum [471] --
6.2.6. Goodness-of-flt tests [475] --
6.2.7. Continuous parameter processes [494] --
6.3. Mixed Spectra [499] --
Chapter 7. Spectral Analysis in Practice [502] --
7.1. Setting up a Spectral Analysis [502] --
7.1.1. The aliasing effect [504] --
7.2. Measures of Precision of Spectral Estimates [570] --
7.3. Resolvability and Bandwidth [513] --
7.3.1. Role of spectral bandwidth [513] --
7.3.2. Role of window bandwidth [517] --
7.4. Design Relations for Spectral Estimation: Choice of Window Parameters, Record Length and Frequency Interval [528] --
7.4.1. Prewhitening and tapering [556] --
7.5. Choice of Window [563] --
7.6. Computation of Spectral Estimates: The Fast Fourier Transform [575] --
7.7. Trend Removal and Seasonal Adjustment: Regression Analysis and Digital Filters [537] --
7.8. Autoregressive, ARMA, and Maximum Entropy Spectral Estimation; CAT Criterion [600] --
7.9. Some Examples [607] --
Chapter 8. Analysis of Processes with Mixed Spectra [613] --
8.1. Nature of the Problem [613] --
8.2. Types of Models [614] --
8.3. Tests Based on the Periodogram [616] --
8.4. The P(A) Test [626] --
8.5. Analysis of Simulated Series [642] --
8.6. Estimation of the Spectral Density Function [648] --
Appendix Ai --

v. 2. Multivariate series, prediction and control.

Volume [2] --
Chapter 9. Multivariate and Multidimensional Processes [654] --
9.1. Correlation and Spectral Properties of Multivariate Stationary Processes [655] --
9.2. Linear Relationships [669] --
9.2.1. Linear relationships with added noise [671] --
9.2.2. The Box-Jenkins “transfer function” model [676] --
9.3. Multiple and Partial Coherency [681] --
9.4. Multivariate AR, MA, and ARMA Models [685] --
9.5. Estimation of Cross-spectra [692] --
9.5.1. Estimation of co-spectra and quadrature spectra [701] --
9.5.2. Estimation of cross-amplitude and phase spectra [702] --
9.5.3. Estimation of coherency --
9.5.4. Practical considerations: alignment techniques [706] --
9.6. Numerical Examples [712] --
9.7. Correlation and Spectral Properties of Multidimensional Processes [718] --
9.7.1. Two-dimensional mixed spectra [724] --
Chapter 10. Prediction, Filtering and Control [727] --
10.1. The Prediction Problem [722] --
10.1.1. Spectral factorization and linear representations [730] --
10.1.2. Geometric representation of linear prediction [736] --
10.1.3. The Kolmogorov approach [738] --
10.1.4. The Wiener approach [748] --
10.1.5. The Wold decomposition [755] --
10.1.6. Prediction for multivariate processes [760] --
10.2. The Box-Jenkins Approach to Forecasting [762] --
10.3. The Filtering Problem [773] --
10.3.1. The Wiener filter [775] --
10.4. Linear Control Systems [780] --
10.4.1. Minimum variance control [781] --
10.4.2. System identification [786] --
10.4.3. Multivariate systems [791] --
10.4.4. State space representations [797] --
10.5. Multivariate Time Series Model Fitting [800] --
10.5.1. Identifiability of multivariate ARMA models [801] --
10.5.2. Fitting state space models via canonical correlations analysis [804] --
10.6. State Space Approach to Forecasting and Filtering Problems: Kalman Filtering [807] --
Chapter 11. Non-stationarity and Non-linearity [816] --
11.1. Spectral Analysis of Non-stationary Processes: Basic Considerations [817] --
11.2. The Theory of Evolutionary Spectra [821] --
11.2.1. Estimation of evolutionary spectra [837] --
11.2.2. Complex demodulation [848] --
11.3. Some Other Definitions of Time-dependent “Spectra” [855] --
11.4. Prediction, Filtering and Control for Non-stationary Processes [859] --
11.5. General Non-linear Models [866] --
11.5.1. Volterra expansions and polyspectra [869] --
11.6. Some Special Non-linear Models: Bilinear, Threshold, and Exponential Models [877] --
References Ri --
Author Index li --
Subject Index Ivii --

MR, REVIEW #

There are no comments on this title.

to post a comment.

Click on an image to view it in the image viewer

¿Necesita ayuda?

Si necesita ayuda para encontrar información, puede visitar personalmente la biblioteca en Av. Alem 1253 Bahía Blanca, llamarnos por teléfono al 291 459 5116, o enviarnos un mensaje a biblioteca.antonio.monteiro@gmail.com

Powered by Koha