• A tool for simulating multi-response linear model data 

      Rimal, Raju; Almøy, Trygve; Sæbø, Solve (Journal article; Peer reviewed, 2018)
      Data science is generating enormous amounts of data, and new and advanced analytical methods are constantly being developed to cope with the challenge of extracting information from such “big-data”. Researchers often use ...
    • Comparison of multi-response estimation methods 

      Rimal, Raju; Almøy, Trygve; Sæbø, Solve (Peer reviewed; Journal article, 2020)
      Prediction performance does not always reflect the estimation behaviour of a method. High error in estimation may necessarily not result in high prediction error, but can lead to an unreliable prediction if test data lie ...
    • Comparison of multi-response prediction methods 

      Rimal, Raju; Almøy, Trygve; Sæbø, Solve (Journal article; Peer reviewed, 2019)
      While data science is battling to extract information from the enormous explosion of data, many estimators and algorithms are being developed for better prediction. Researchers and data scientists often introduce new methods ...
    • Evaluation of models for predicting the average monthly Euro versus Norwegian krone exchange rate from financial and commodity information 

      Rimal, Raju (Master thesis, 2015-05-12)
      Many multinational companies and policy makers carry out decisions by speculat- ing exchange rate. Exchange rate is determined by the demand and supply of a currency. It depends highly on variables like imports, exports, ...
    • Exploration of multi-response multivariate methods 

      Rimal, Raju (PhD Thesis;2019:76, Doctoral thesis, 2019)
      A linear regression model defines a linear relationship between two or more random variables. The random variables that depend on other random variables are often called response variables and the independent random variables ...