Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Minimum Distance Estimation on Time Series Analysis With Little Data

Minimum Distance Estimation on Time Series Analysis With Little Data

Tekin, Hakan

Paperback

EconomicsGeneral EducationProbability & Statistics

ISBN10: 1288327714
ISBN13: 9781288327713
Publisher: Biblioscholar
Published: Nov 21 2012
Pages: 102
Weight: 0.34
Height: 0.21 Width: 6.14 Depth: 9.21
Language: English

Minimum distance estimate is a statistical parameter estimate technique that selects model parameters that minimize a good-of-fit statistic. Minimum distance estimation has been demonstrated better standard approaches, including maximum likelihood estimators and least squares, in estimating statistical distribution parameters with very small data sets. This research applies minimum distance estimation to the task of making time series predictions with very few historical observations. In a Monte Carlo analysis, we test a variety of distance measures and report the results based on many different criteria. Our analysis tests the robustness of the approach by testing its ability to make predictions when the fitted time-series model does not match the data generation model. Our analysis indicates benefits in applying minimum distance estimation when making time series prediction based on less than 30 observations.

Also in

General Education

View All