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Prediction Problems for Square-Transformed Stationary Processes

Choi, In-Bong; Taniguchi, Masanobu - In: Statistical Inference for Stochastic Processes 6 (2003) 1, pp. 43-64

Persistent link: https://www.econbiz.de/10005169133

Global property of error density estimation in nonlinear autoregressive time series models

Cheng, Fuxia - In: Statistical Inference for Stochastic Processes 13 (2010) 1, pp. 43-53

Persistent link: https://www.econbiz.de/10008533935

Efficient Estimation in a Semiparametric Autoregressive Model

Schick, Anton - In: Statistical Inference for Stochastic Processes 2 (1999) 1, pp. 69-98

Persistent link: https://www.econbiz.de/10005391499

Classical Method of Moments for Partially and Discretely Observed Ergodic Models

Masuda, Hiroki - In: Statistical Inference for Stochastic Processes 8 (2005) 1, pp. 25-50

Persistent link: https://www.econbiz.de/10005615997

A least square-type procedure for parameter estimation in stochastic differential equations with additive fractional noise

Neuenkirch, Andreas; Tindel, Samy - In: Statistical Inference for Stochastic Processes 17 (2014) 1, pp. 99-120

We study a least square-type estimator for an unknown parameter in the drift coefficient of a stochastic differential equation with additive fractional noise of Hurst parameter <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$H1/2$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <mi>H</mi> <mo></mo> <mn>1</mn> <mo stretchy="false">/</mo> <mn>2</mn> </mrow> </math> </EquationSource> </InlineEquation>. The estimator is based on discrete time observations of the stochastic differential...</equationsource></equationsource></inlineequation>

Persistent link: https://www.econbiz.de/10010992891