Time series econometrics  

Sparse Bayesian Learning

Wavelet methods

Statistical machine learning 

Statistical Surveillance

Lecturer and course responsible:

STAT250(V22 Cooperate with Pekka Parviainen):Monte Carlo Methods and Bayesian Statistics

STAT260(H20,H21,H23)/STATLEARN(H17,H18, H19): Statistical learning 

STAT240 (V17,V21,V23), UIB: Theory of Finance

STAT231 (H16,H20,H22,H24), UIB: Nonlife insurance mathematics

STAT111 (V16, V18), UIB: Statistiske metoder (Bachelor level. Given in Norwegian)

STAT250 (H15), UIB: Monte Carlo methods in statistics

ECO403 (V15,V14), NHH: Time series analysis and prediction

MAT013 (H14), NHH:Matematisk statistikk (Bachelor level. Given in Norwegian)

External grading sensor (2017-):

ENE473 Real Options Analysis of Electricity Markets, NHH 

BEA525 Financial Engineering in Energy Markets using Real Options, NHH

GRA 4136/41363 Predictive Analytics and Machine Learning/ Machine Learning for Business, BI OSLO

TMA4268 Statistisk læring, NTNU

TMA4900  Industriell matematikk /Datateknologi, masteroppgave, NTNU

IT3920/3903  Masteroppgave for MSIT/ Masteroppgave i informatikk: Kunstig intelligens, NTNU

Supervised Ph.D. project:

(June 2023, UIB)  Ingvild M. Helgøy,  Sparse Bayesian learning methods and statistical survival models

Supervised Master projects:

(V12 Lund University) Simon Reese: “Are tests for smooth structural change affected by data inaccuracies?”, Co-supervisor: Fredrik N G Andersson

(V15 NHH) Midtdal S. Tollefsen &  Hans Thomas: "En analyse av regionale prisforskjeller i det norske boligmarkedet : en tidsserieanalyse 1993-2013" , Co-supervisor: Ola Honningdal Grytten

(V18 UIB) Therese Grindheim: "Time Series: Forecasting and Evaluation Methods With Concentration On Evaluation Methods for Density Forecasting"

(H18 UIB) Victoria Foster: Empirical time series analysis with focus on wavelet methods and economic data from Norway

(V19 UIB) Francine D. D. Rogowski: A Comprehensive Study of Kernels and Feature Selection in Support Vector Regression, Co-supervisor: Bjørn Gunnar Hansen

(H19 UIB) Fredrik H. Bentsen: Model Construction with Support Vector Machines and Gaussian Processes through Kernel Search

(V20 UIB) Elise F.F. Isaksen:  Generative and Discriminative Classifiers: from Theory to Implementation

(V21 UIB) Sandra Heimsæter: A Dimensionality Reducing Extension of Bayesian Relevance Learning, Co-supervisor: Ingvild M. Helgøy

(V21 UIB) Arne L. Waagbø: APARCH Models Estimated by Support Vector Regression

(V23 UIB) Mathias E. Ostnes: Modern Variable Selection Methods with Empirical Analysis,Co-supervisor: Ingvild M. Helgøy

2017-2022: 6 bachelor thesis (STAT292 UIB Project in Statistics)

• Hyunjoo Kim Karlsson , Yushu Li (2026) 
  •  Investigation of Swedish Krona exchange rate volatility using APARCH-Support Vector Regression. Financial innovation, Volume 12, article number 7
• Ingvild M. Helgøy, Hans J. Skaug, Yushu Li (2024)
  • Sparse Bayesian learning using TMB (Template Model Builder), Statistics and Computing, Volume 34, article number 173, Springer
• Bjørn Gunnar Hansen, Yushu Li, Ruohao Sun, Ingunn Schei (2024)
  • Forecasting milk delivery to dairy – How modern statistical and machine learning methods can contribute, (on line 15 Feb.2024), Expert Systems with Applications, Elsevier
• Ingvild M. Helgøy and Yushu Li (2023)
  • A Bayesian Lasso based Sparse Learning Model. (Online, 26 Oct 2023), 
    Communications in Statistics - Simulation and Computation, Taylor & Francis
• Yushu Li and Hyunjoo Kim Karlsson (2022)
  • Investigating the Asymmetric Behavior of Oil Price Volatility Using Support Vector Regression,  (Online, May 6, 2022), Computational Economics, Springer
• Yushu Li and Fredrik N.G. Andersson (2021)
  • A simple wavelet-based test for serial correlation in panel data models, Empirical Economics, 60, pp. 2351-2363 , Springer
• Fredrik N.G. Andersson and Yushu Li (2020)
  • Are Central Bankers Inflation Nutters? An MCMC Estimator of the Long-Memory Parameter in a State Space Model, Computational Economics, 55, pp. 529-549, Springer
• Yushu Li and Jonas Andersson (2019)
  • A Likelihood Ratio and Markov Chain Based Method to Evaluate Density Forecasting (Online, May 17, 2019), Journal of Forecasting,  Wiley
• Hyunjoo Kim Karlsson, Yushu Li and Ghazi Shukur (2018)
  • The Causal Nexus between Oil Prices, Interest Rates, and Unemployment in Norway Using Wavelet Methods, Sustainability, 10(8), p. 2792
• Bjørn Gunnar Hansen and Yushu Li (2017)
  • An Analysis of Past World Market Prices of Feed and Milk and Predictions for the Future, Agribusiness, 33(2), pp. 175-193, Wiley
• Simon Reese and Yushu Li (2015)
  • Testing for structural breaks in the presence of data perturbations: impacts and wavelet-based improvements, Journal of Statistical Computation and Simulation, 85(17), pp. 3468-3479, Taylor & Francis
• Yushu Li (2015)
  • Estimate Long Memory Causality Relationship by Wavelet Method, Computational Economics, 45, pp. 531-544, Springer
• Yushu Li (2014)
  • Estimating and Forecasting APARCH-Skew-t Model by Wavelet Support Vector Machines, Journal of Forecasting, 33(4), pp. 259-269, Wiley
• Yushu Li and Simon Reese (2014)
  • Wavelet improvement in turning point detection using a hidden Markov model: from the aspects of cyclical identification and outlier correction, Computational Statistics, 29, pp.1481-1496, Springer
• Yushu Li (2013)
  • Wavelet based outlier correction for power controlled turning point detection in surveillance systems, Economic Modeling, 30, pp. 317-321, Elsevier
• Yushu Li and Shukur Ghazi (2013)
  • Testing for Unit Roots in Panel Data Using a Wavelet Ratio Method, Computational Economics, 41, pp. 59-69, Springer
• Yushu Li and Shukur Ghazi (2011)
  • Linear and Nonlinear Causality Test in LSTAR Models: Wavelet Decomposition in Nonlinear Environment, Journal of Statistical Computation and Simulation, 81(12), pp.1913-1925, Taylor & Francis
• Yushu Li and Shukur Ghazi (2011)
  • Wavelet Improvement of the Over-Rejection of Unit Root Test Under GARCH Errors: An Application to Swedish Immigration Data, Communications in Statistics, Theory and Methods, 40(13), pp. 2385-2396, Taylor & Francis
• Yushu Li and Shukur Ghazi (2010)
  • Testing for Unit Root Against LSTAR Model: Wavelet Improvement Under GARCH Distortion, Communications in Statistics, Simulation and Computation, 39(2), pp. 277-286, Taylor & Francis

Involved Research Projects

2018-2021 “Strategic Risk Adoption in Real Options under Multi-Horizon Regime Switching and Uncertainty” (project number 274569). Funded by Finance Market Fund, Norwegian research council, project leader Yushu Li.

2020- 2023 “Assimilating 4D Seismic Data: Big Data into Big Models”. Funded by Research Council of Norway Petromaks-2, project leader Dean Oliver, NORCE.

2021- “Digital technology for personalised management and therapy of hypertensive nephropathy”. Funded by Helse Vest, project leader Hans-Peter Marti, Department of Medicine, UIB

2021- 2022 “Predicting Milk Production with Automated Milking System Data”. Funded by Forskningsmidlene for jordbruk og matindustri, project leader Ruohao Sun, Tine SA      

 Educational Project

2022-2023 Utvikling av felleskurs og deling og utvikling av undervisning og læring i statistikk/ datascience/ maskinlæring, funded by UHR-MNT. Reference no. of commitment letter: 21/135-9, project responsible person Yushu Li  

2024 National and International Network building for Actuarial Data Science Educations and Research at Department of Mathematics (MI), Bergen universitetsfond, project responsible person Yushu Li                                                                     

Short CV