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Missouri




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Missouri Chief's Ouster Sparks Political, Legal Aftershocks

The state's Republican governor is in a pitched battle with the state's educators over the process he used to fire Missouri's commissioner of education.




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Missouri's State Board Hasn't Met Since January. With Governor Gone, What Now?

Gov. Erik Greitens has resigned and the board doesn't have enough governor-appointed members to form a quorum. Important tasks have been piling up.




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Revamped School Board Starts Search for New Schools Chief for Missouri

The search for Missouri's next top education official has begun nearly 10 months after the last one was fired. The state board of education began accepting applications last week.




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Missouri Tackles Challenge of Dyslexia Screening, Services

New state mandates start next school year aimed at identifying and supporting students with dyslexia. The 2016 law also led to development of training for teachers.




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High Court Declines Missouri District's Appeal Over At-Large Board Voting

The justices declined to hear the appeal of the Ferguson-Florissant district over its at-large board elections, which lower courts invalidated as violating the Voting Rights Act.




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After Protracted Political Spat, Missouri Rehires Fired State Schools Chief

Former Republican Missouri Gov. Eric Greitens appointed enough board members to have Commissioner Margie Vandeven fired last year, but now that he's gone, the state board decided to hire her back.




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Q&A: How to Bolster Cybersecurity in Your Schools

Melissa Tebbenkamp, the director of instructional technology for the Raytown Quality Schools near Kansas City, says her district's biggest cybersecurity risk is "ourselves." She outlines what it takes to teach educators how to help protect schools and districts against cyberattacks.




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Missouri State School Board Rehires Fired Commissioner

Former Missouri education Commissioner Margie Vandeven, who was fired by by the state's board of education, has been rehired.




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Educational Opportunities and Performance in Missouri

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.




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Educational Opportunities and Performance in Missouri

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.




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Missouri National Guard to help hand out school meals




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Missouri teachers virtually educate students about pandemic




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Étude hygienique sur la profession de mouleur en cuivre : pour servir a l'histoire des professions exposées aux poussières inorganiques / par Ambroise Tardieu.

Paris, [France] : J.B. Baillière, Libraire de l'Académie Impériale de Médicine, 1854.




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10 Alternative mental health resources




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Resilient & resisting: Hackney Museum our stories




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Your thoughts are not facts.

[London] : [publisher not identified], [2019]




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[Our times : contagious cities]

[Hong Kong] : [Art In Hospitals], [2019]




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Drug abuse information source book / [Foreword by Edward S. Brady].

[West Point, Pa.] : [Merck Sharp & Dohme], [1977?]




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Sydney Wiese, recovering from coronavirus, continually talking with friends and family: 'Our world is uniting'

Hear how former Oregon State guard and current member of the WNBA's LA Sparks Sydney Wiese is recovering from a COVID-19 diagnosis, seeing friends and family show support and love during a trying time.




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Inside Sabrina Ionescu and Ruthy Hebard's lasting bond on quick look of 'Our Stories'

Learn how Oregon stars Sabrina Ionescu and Ruthy Hebard developed a lasting bond as college freshmen and carried that through storied four-year careers for the Ducks. Watch "Our Stories Unfinished Business: Sabrina Ionescu and Ruthy Hebard" debuting Wednesday, April 15 at 7 p.m. PT/ 8 p.m. MT on Pac-12 Network.




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Dr. Michelle Tom shares journey from ASU women's hoops to treating COVID-19 patients

Pac-12 Networks' Ashley Adamson speaks with former Arizona State women's basketball player Michelle Tom, who is now a doctor treating COVID-19 patients Winslow Indian Health Care Center and Little Colorado Medical Center in Eastern Arizona.




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Natalie Chou breaks through stereotypes, inspires young Asian American girls on 'Our Stories' quick look

Watch the debut of "Our Stories - Natalie Chou" on Sunday, May 10 at 12:30 p.m. PT/ 1:30 p.m. MT on Pac-12 Network.




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TIGER: using artificial intelligence to discover our collections

The State Library of NSW has almost 4 million digital files in its collection.




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Have your say on the Highway 404 Employment Corridor Secondary Plan




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Share your fall and winter photos with us!




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Unlikeness is us : fourteen from the Exeter book

Exeter book. Selections. English
9781554471751 (softcover)




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Documenting rebellions : a study of four lesbian and gay archives in queer times

Sheffield, Rebecka Taves, author.
9781634000918 paperback




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The ARMA alphabet soup: A tour of ARMA model variants

Scott H. Holan, Robert Lund, Ginger Davis

Source: Statist. Surv., Volume 4, 232--274.

Abstract:
Autoregressive moving-average (ARMA) difference equations are ubiquitous models for short memory time series and have parsimoniously described many stationary series. Variants of ARMA models have been proposed to describe more exotic series features such as long memory autocovariances, periodic autocovariances, and count support set structures. This review paper enumerates, compares, and contrasts the common variants of ARMA models in today’s literature. After the basic properties of ARMA models are reviewed, we tour ARMA variants that describe seasonal features, long memory behavior, multivariate series, changing variances (stochastic volatility) and integer counts. A list of ARMA variant acronyms is provided.

References:
Aknouche, A. and Guerbyenne, H. (2006). Recursive estimation of GARCH models. Communications in Statistics-Simulation and Computation 35 925–938.

Alzaid, A. A. and Al-Osh, M. (1990). An integer-valued pth-order autoregressive structure (INAR (p)) process. Journal of Applied Probability 27 314–324.

Anderson, P. L., Tesfaye, Y. G. and Meerschaert, M. M. (2007). Fourier-PARMA models and their application to river flows. Journal of Hydrologic Engineering 12 462–472.

Ansley, C. F. (1979). An algorithm for the exact likelihood of a mixed autoregressive-moving average process. Biometrika 66 59–65.

Basawa, I. V. and Lund, R. (2001). Large sample properties of parameter estimates for periodic ARMA models. Journal of Time Series Analysis 22 651–663.

Bauwens, L., Laurent, S. and Rombouts, J. V. K. (2006). Multivariate GARCH models: A survey. Journal of Applied Econometrics 21 79–109.

Bertelli, S. and Caporin, M. (2002). A note on calculating autocovariances of long-memory processes. Journal of Time Series Analysis 23 503–508.

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31 307–327.

Bollerslev, T. (2008). Glossary to ARCH (GARCH). CREATES Research Paper 2008-49.

Bollerslev, T., Engle, R. F. and Wooldridge, J. M. (1988). A capital asset pricing model with time-varying covariances. The Journal of Political Economy 96 116–131.

Bondon, P. and Palma, W. (2007). A class of antipersistent processes. Journal of Time Series Analysis 28 261–273.

Bougerol, P. and Picard, N. (1992). Strict stationarity of generalized autoregressive processes. The Annals of Probability 20 1714–1730.

Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (2008). Time Series Analysis: Forecasting and Control, 4th ed. Wiley, New Jersey.

Breidt, F. J., Davis, R. A. and Trindade, A. A. (2001). Least absolute deviation estimation for all-pass time series models. Annals of Statistics 29 919–946.

Brockwell, P. J. (1994). On continuous-time threshold ARMA processes. Journal of Statistical Planning and Inference 39 291–303.

Brockwell, P. J. (2001). Continuous-time ARMA processes. In Stochastic Processes: Theory and Methods, ( D. N. Shanbhag and C. R. Rao, eds.). Handbook of Statistics 19 249–276. Elsevier.

Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, 2nd ed. Springer, New York.

Brockwell, P. J. and Davis, R. A. (2002). Introduction to Time Series and Forecasting, 2nd ed. Springer, New York.

Brockwell, P. J. and Marquardt, T. (2005). Lèvy-driven and fractionally integrated ARMA processes with continuous-time paramaters. Statistica Sinica 15 477–494.

Chan, K. S. (1990). Testing for threshold autoregression. Annals of Statistics 18 1886–1894.

Chan, N. H. (2002). Time Series: Applications to Finance. John Wiley & Sons, New York.

Chan, N. H. and Palma, W. (1998). State space modeling of long-memory processes. Annals of Statistics 26 719–740.

Chan, N. H. and Palma, W. (2006). Estimation of long-memory time series models: A survey of different likelihood-based methods. Advances in Econometrics 20 89–121.

Chatfield, C. (2003). The Analysis of Time Series: An Introduction, 6th ed. Chapman & Hall/CRC, Boca Raton.

Chen, W., Hurvich, C. M. and Lu, Y. (2006). On the correlation matrix of the discrete Fourier transform and the fast solution of large Toeplitz systems for long-memory time series. Journal of the American Statistical Association 101 812–822.

Chernick, M. R., Hsing, T. and McCormick, W. P. (1991). Calculating the extremal index for a class of stationary sequences. Advances in Applied Probability 23 835–850.

Chib, S., Nardari, F. and Shephard, N. (2006). Analysis of high dimensional multivariate stochastic volatility models. Journal of Econometrics 134 341–371.

Cryer, J. D. and Chan, K. S. (2008). Time Series Analysis: With Applications in R. Springer, New York.

Cui, Y. and Lund, R. (2009). A new look at time series of counts. Biometrika 96 781–792.

Davis, R. A., Dunsmuir, W. T. M. and Wang, Y. (1999). Modeling time series of count data. In Asymptotics, Nonparametrics and Time Series, ( S. Ghosh, ed.). Statistics Textbooks Monograph 63–113. Marcel Dekker, New York.

Davis, R. A., Dunsmuir, W. and Streett, S. B. (2003). Observation-driven models for Poisson counts. Biometrika 90 777–790.

Davis, R. A. and Resnick, S. I. (1996). Limit theory for bilinear processes with heavy-tailed noise. The Annals of Applied Probability 6 1191–1210.

Deistler, M. and Hannan, E. J. (1981). Some properties of the parameterization of ARMA systems with unknown order. Journal of Multivariate Analysis 11 474–484.

Dufour, J. M. and Jouini, T. (2005). Asymptotic distribution of a simple linear estimator for VARMA models in echelon form. Statistical Modeling and Analysis for Complex Data Problems 209–240.

Dunsmuir, W. and Hannan, E. J. (1976). Vector linear time series models. Advances in Applied Probability 8 339–364.

Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford University Press, Oxford.

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50 987–1007.

Engle, R. F. (2002). Dynamic conditional correlation. Journal of Business and Economic Statistics 20 339–350.

Engle, R. F. and Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric Reviews 5 1–50.

Fuller, W. A. (1996). Introduction to Statistical Time Series, 2nd ed. John Wiley & Sons, New York.

Geweke, J. and Porter-Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis 4 221–238.

Gladyšhev, E. G. (1961). Periodically correlated random sequences. Soviet Math 2 385–388.

Granger, C. W. J. (1982). Acronyms in time series analysis (ATSA). Journal of Time Series Analysis 3 103–107.

Granger, C. W. J. and Andersen, A. P. (1978). An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht Göttingen.

Granger, C. W. J. and Joyeux, R. (1980). An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis 1 15–29.

Gray, H. L., Zhang, N. F. and Woodward, W. A. (1989). On generalized fractional processes. Journal of Time Series Analysis 10 233–257.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press, Princeton, New Jersey.

Hannan, E. J. (1955). A test for singularities in Sydney rainfall. Australian Journal of Physics 8 289–297.

Hannan, E. J. (1969). The identification of vector mixed autoregressive-moving average system. Biometrika 56 223–225.

Hannan, E. J. (1970). Multiple Time Series. John Wiley & Sons, New York.

Hannan, E. J. (1976). The identification and parameterization of ARMAX and state space forms. Econometrica 44 713–723.

Hannan, E. J. (1979). The Statistical Theory of Linear Systems. In Developments in Statistics ( P. R. Krishnaiah, ed.) 83–121. Academic Press, New York.

Hannan, E. J. and Deistler, M. (1987). The Statistical Theory of Linear Systems. John Wiley & Sons, New York.

Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge.

Haslett, J. and Raftery, A. E. (1989). Space-time modelling with long-memory dependence: Assessing Ireland’s wind power resource. Applied Statistics 38 1–50.

Hosking, J. R. M. (1981). Fractional differencing. Biometrika 68 165–176.

Hui, Y. V. and Li, W. K. (1995). On fractionally differenced periodic processes. Sankhyā: The Indian Journal of Statistics, Series B 57 19–31.

Jacobs, P. A. and Lewis, P. A. W. (1978a). Discrete time series generated by mixtures. I: Correlational and runs properties. Journal of the Royal Statistical Society. Series B (Methodological) 40 94–105.

Jacobs, P. A. and Lewis, P. A. W. (1978b). Discrete time series generated by mixtures II: Asymptotic properties. Journal of the Royal Statistical Society. Series B (Methodological) 40 222–228.

Jacobs, P. A. and Lewis, P. A. W. (1983). Stationary discrete autoregressive-moving average time series generated by mixtures. Journal of Time Series Analysis 4 19–36.

Jones, R. H. (1980). Maximum likelihood fitting of ARMA models to time series with missing observations. Technometrics 22 389–395.

Jones, R. H. and Brelsford, W. M. (1967). Time series with periodic structure. Biometrika 54 403–408.

Kedem, B. and Fokianos, K. (2002). Regression Models for Time Series Analysis. John Wiley & Sons, New Jersey.

Ko, K. and Vannucci, M. (2006). Bayesian wavelet-based methods for the detection of multiple changes of the long memory parameter. IEEE Transactions on Signal Processing 54 4461–4470.

Kohn, R. (1979). Asymptotic estimation and hypothesis testing results for vector linear time series models. Econometrica 47 1005–1030.

Kokoszka, P. S. and Taqqu, M. S. (1995). Fractional ARIMA with stable innovations. Stochastic Processes and their Applications 60 19–47.

Kokoszka, P. S. and Taqqu, M. S. (1996). Parameter estimation for infinite variance fractional ARIMA. Annals of Statistics 24 1880–1913.

Lawrance, A. J. and Lewis, P. A. W. (1980). The exponential autoregressive-moving average EARMA(p,q) process. Journal of the Royal Statistical Society. Series B (Methodological) 42 150–161.

Ling, S. and Li, W. K. (1997). On fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity. Journal of the American Statistical Association 92 1184–1194.

Liu, J. and Brockwell, P. J. (1988). On the general bilinear time series model. Journal of Applied Probability 25 553–564.

Lund, R. and Basawa, I. V. (2000). Recursive prediction and likelihood evaluation for periodic ARMA models. Journal of Time Series Analysis 21 75–93.

Lund, R., Shao, Q. and Basawa, I. (2006). Parsimonious periodic time series modeling. Australian & New Zealand Journal of Statistics 48 33–47.

Lütkepohl, H. (1991). Introduction to Multiple Time Series Analysis. Springer-Verlag, New York.

Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer, New York.

MacDonald, I. L. and Zucchini, W. (1997). Hidden Markov and Other Models for Discrete-Valued Time Series. Chapman & Hall/CRC, Boca Raton.

Mann, H. B. and Wald, A. (1943). On the statistical treatment of linear stochastic difference equations. Econometrica 11 173–220.

Marriott, J., Ravishanker, N., Gelfand, A. and Pai, J. (1996). Bayesian analysis of ARMA processes: Complete sampling-based inference under exact likelihoods. In Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner ( D. Berry, K. Challoner and J. Geweke, eds.) 243–256. Wiley, New York.

McKenzie, E. (1988). Some ARMA models for dependent sequences of Poisson counts. Advances in Applied Probability 20 822–835.

Mikosch, T. and Starica, C. (2004). Nonstationarities in financial time series, the long-range dependence, and the IGARCH effects. Review of Economics and Statistics 86 378–390.

Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59 347–370.

Nelson, D. B. and Cao, C. Q. (1992). Inequality constraints in the univariate GARCH model. Journal of Business and Economic Statistics 10 229–235.

Ooms, M. and Franses, P. H. (2001). A seasonal periodic long memory model for monthly river flows. Environmental Modelling & Software 16 559–569.

Pagano, M. (1978). On periodic and multiple autoregressions. Annals of Statistics 6 1310–1317.

Pai, J. S. and Ravishanker, N. (1998). Bayesian analysis of autoregressive fractionally integrated moving-average processes. Journal of Time Series Analysis 19 99–112.

Palma, W. (2007). Long-Memory Time Series: Theory and Methods. John Wiley & Sons, New Jersey.

Palma, W. and Chan, N. H. (2005). Efficient estimation of seasonal long-range-dependent processes. Journal of Time Series Analysis 26 863–892.

Pfeifer, P. E. and Deutsch, S. J. (1980). A three-stage iterative procedure for space-time modeling. Technometrics 22 35–47.

Prado, R. and West, M. (2010). Time Series Modeling, Computation and Inference. Chapman & Hall/CRC, Boca Raton.

Quoreshi, A. M. M. S. (2008). A long memory count data time series model for financial application. Preprint.

R Development Core Team, (2010). R: A Language and Environment for Statistical Computing. http://www.R-project.org.

Ravishanker, N. and Ray, B. K. (1997). Bayesian analysis of vector ARMA models using Gibbs sampling. Journal of Forecasting 16 177–194.

Ravishanker, N. and Ray, B. K. (2002). Bayesian prediction for vector ARFIMA processes. International Journal of Forecasting 18 207–214.

Reinsel, G. C. (1997). Elements of Multivariate Time Series Analysis. Springer, New York.

Resnick, S. I. and Willekens, E. (1991). Moving averages with random coefficients and random coefficient autoregressive models. Communications in Statistics. Stochastic Models 7 511–525.

Rootzén, H. (1986). Extreme value theory for moving average processes. The Annals of Probability 14 612–652.

Scotto, M. G. (2007). Extremes for solutions to stochastic difference equations with regularly varying tails. REVSTAT–Statistical Journal 5 229–247.

Shao, Q. and Lund, R. (2004). Computation and characterization of autocorrelations and partial autocorrelations in periodic ARMA models. Journal of Time Series Analysis 25 359–372.

Shumway, R. H. and Stoffer, D. S. (2006). Time Series Analysis and its Applications: With R Examples, 2nd ed. Springer, New York.

Silvennoinen, A. and Teräsvirta, T. (2009). Multivariate GARCH models. In Handbook of Financial Time Series ( T. Andersen, R. Davis, J. Kreib, and T. Mikosch, eds.) Springer, New York.

Sowell, F. (1992). Maximum likelihood estimation of stationary univariate fractionally integrated time series models. Journal of Econometrics 53 165–188.

Startz, R. (2008). Binomial autoregressive moving average models with an application to U.S. recessions. Journal of Business and Economic Statistics 26 1–8.

Stramer, O., Tweedie, R. L. and Brockwell, P. J. (1996). Existence and stability of continuous time threshold ARMA processes. Statistica Sinica 6 715–732.

Subba Rao, T. (1981). On the theory of bilinear time series models. Journal of the Royal Statistical Society. Series B (Methodological) 43 244–255.

Tong, H. and Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological) 42 245–292.

Troutman, B. M. (1979). Some results in periodic autoregression. Biometrika 66 219–228.

Tsai, H. (2009). On continuous-time autoregressive fractionally integrated moving average processes. Bernoulli 15 178–194.

Tsai, H. and Chan, K. S. (2000). A note on the covariance structure of a continuous-time ARMA process. Statistica Sinica 10 989–998.

Tsai, H. and Chan, K. S. (2005). Maximum likelihood estimation of linear continuous time long memory processes with discrete time data. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 67 703–716.

Tsai, H. and Chan, K. S. (2008). A note on inequality constraints in the GARCH model. Econometric Theory 24 823–828.

Tsay, R. S. (1989). Parsimonious parameterization of vector autoregressive moving average models. Journal of Business and Economic Statistics 7 327–341.

Tunnicliffe-Wilson, G. (1979). Some efficient computational procedures for high order ARMA models. Journal of Statistical Computation and Simulation 8 301–309.

Ursu, E. and Duchesne, P. (2009). On modelling and diagnostic checking of vector periodic autoregressive time series models. Journal of Time Series Analysis 30 70–96.

Vecchia, A. V. (1985a). Maximum likelihood estimation for periodic autoregressive moving average models. Technometrics 27 375–384.

Vecchia, A. V. (1985b). Periodic autoregressive-moving average (PARMA) modeling with applications to water resources. Journal of the American Water Resources Association 21 721–730.

Vidakovic, B. (1999). Statistical Modeling by Wavelets. John Wiley & Sons, New York.

West, M. and Harrison, J. (1997). Bayesian Forecasting and Dynamic Models, 2nd ed. Springer, New York.

Wold, H. (1954). A Study in the Analysis of Stationary Time Series. Almquist & Wiksell, Stockholm.

Woodward, W. A., Cheng, Q. C. and Gray, H. L. (1998). A k-factor GARMA long-memory model. Journal of Time Series Analysis 19 485–504.

Zivot, E. and Wang, J. (2006). Modeling Financial Time Series with S-PLUS, 2nd ed. Springer, New York.




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Start your Chinese Family Search at the State Library of...

Start your Chinese Family Search at the State Library of NSW   One in ten Sydneysiders claims Chinese ancestry




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Was one of your ancestors a whaler?

Whaling – along with wool production – was one of the first primary industries after the establishment of New South Wa




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Was your ancestor a doctor?

A register of medical practitioners was first required to be kept in 1838 in New South Wales  and was published in the G




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Know Your Clients' behaviours: a cluster analysis of financial transactions. (arXiv:2005.03625v1 [econ.EM])

In Canada, financial advisors and dealers by provincial securities commissions, and those self-regulatory organizations charged with direct regulation over investment dealers and mutual fund dealers, respectively to collect and maintain Know Your Client (KYC) information, such as their age or risk tolerance, for investor accounts. With this information, investors, under their advisor's guidance, make decisions on their investments which are presumed to be beneficial to their investment goals. Our unique dataset is provided by a financial investment dealer with over 50,000 accounts for over 23,000 clients. We use a modified behavioural finance recency, frequency, monetary model for engineering features that quantify investor behaviours, and machine learning clustering algorithms to find groups of investors that behave similarly. We show that the KYC information collected does not explain client behaviours, whereas trade and transaction frequency and volume are most informative. We believe the results shown herein encourage financial regulators and advisors to use more advanced metrics to better understand and predict investor behaviours.




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A stochastic user-operator assignment game for microtransit service evaluation: A case study of Kussbus in Luxembourg. (arXiv:2005.03465v1 [physics.soc-ph])

This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users' travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.




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Turn your ‘iso’ moments into history

Thursday 9 April 2020
The State Library wants your self-isolation images to become part of the historic record.




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Add your entry to the great pandemic diary of 2020

Monday 4 May 2020
The State Library wants to capture the thoughts and feelings of the State via a new diary sharing platform launched TODAY.




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COVID-19 in-language resources




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The Library wants your self-isolation images

The State Library launched a new collecting drive on Instagram today called #NSWathome to ensure your self-isolation images become part of the historic record.




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The complexity of bird behaviour : a facet theory approach

Hackett, Paul, 1960- author
9783030121921 (electronic bk.)




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The bitter gourd genome

9783030150624




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Rediscovery of genetic and genomic resources for future food security

9811501564




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Governance of offshore freshwater resources

Martin-Nagle, Renee, author.
9004421041 (electronic book)




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Current developments in biotechnology and bioengineering : resource recovery from wastes

0444643222




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Computer security : ESORICS 2019 International Workshops, IOSec, MSTEC, and FINSEC, Luxembourg City, Luxembourg, September 26-27, 2019, Revised Selected Papers

European Symposium on Research in Computer Security (24th : 2019 : Luxembourg, Luxembourg)
9783030420512 (electronic bk.)




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Beyond our genes : pathophysiology of gene and environment interaction and epigenetic inheritance

9783030352134 (electronic bk.)




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African edible insects as alternative source of food, oil, protein and bioactive components

9783030329525 (electronic bk.)





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A statistical analysis of noisy crowdsourced weather data

Arnab Chakraborty, Soumendra Nath Lahiri, Alyson Wilson.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 116--142.

Abstract:
Spatial prediction of weather elements like temperature, precipitation, and barometric pressure are generally based on satellite imagery or data collected at ground stations. None of these data provide information at a more granular or “hyperlocal” resolution. On the other hand, crowdsourced weather data, which are captured by sensors installed on mobile devices and gathered by weather-related mobile apps like WeatherSignal and AccuWeather, can serve as potential data sources for analyzing environmental processes at a hyperlocal resolution. However, due to the low quality of the sensors and the nonlaboratory environment, the quality of the observations in crowdsourced data is compromised. This paper describes methods to improve hyperlocal spatial prediction using this varying-quality, noisy crowdsourced information. We introduce a reliability metric, namely Veracity Score (VS), to assess the quality of the crowdsourced observations using a coarser, but high-quality, reference data. A VS-based methodology to analyze noisy spatial data is proposed and evaluated through extensive simulations. The merits of the proposed approach are illustrated through case studies analyzing crowdsourced daily average ambient temperature readings for one day in the contiguous United States.




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Sojourn time dimensions of fractional Brownian motion

Ivan Nourdin, Giovanni Peccati, Stéphane Seuret.

Source: Bernoulli, Volume 26, Number 3, 1619--1634.

Abstract:
We describe the size of the sets of sojourn times $E_{gamma }={tgeq 0:|B_{t}|leq t^{gamma }}$ associated with a fractional Brownian motion $B$ in terms of various large scale dimensions.




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Recurrence of multidimensional persistent random walks. Fourier and series criteria

Peggy Cénac, Basile de Loynes, Yoann Offret, Arnaud Rousselle.

Source: Bernoulli, Volume 26, Number 2, 858--892.

Abstract:
The recurrence and transience of persistent random walks built from variable length Markov chains are investigated. It turns out that these stochastic processes can be seen as Lévy walks for which the persistence times depend on some internal Markov chain: they admit Markov random walk skeletons. A recurrence versus transience dichotomy is highlighted. Assuming the positive recurrence of the driving chain, a sufficient Fourier criterion for the recurrence, close to the usual Chung–Fuchs one, is given and a series criterion is derived. The key tool is the Nagaev–Guivarc’h method. Finally, we focus on particular two-dimensional persistent random walks, including directionally reinforced random walks, for which necessary and sufficient Fourier and series criteria are obtained. Inspired by ( Adv. Math. 208 (2007) 680–698), we produce a genuine counterexample to the conjecture of ( Adv. Math. 117 (1996) 239–252). As for the one-dimensional case studied in ( J. Theoret. Probab. 31 (2018) 232–243), it is easier for a persistent random walk than its skeleton to be recurrent. However, such examples are much more difficult to exhibit in the higher dimensional context. These results are based on a surprisingly novel – to our knowledge – upper bound for the Lévy concentration function associated with symmetric distributions.