Linear Wavelet-Based Estimators of Partial Derivatives of Multivariate Density Function for Stationary and Ergodic Continuous Time Processes
Abstract
:1. Introduction
Notation
2. Mathematical Background
2.1. Linear Wavelet Estimator
2.2. Besov Spaces
- (B.1)
- (B.2)
2.3. Linear Wavelet Estimator
3. Assumptions and Main Results
- (C.1)
- For every , the sequence
- (C.2)
- Moreover, for every ,
- (C.3)
- The partial derivative function belongs to the Besov space for some , .
- (C.4)
- For all values of , the partial derivative function belongs to the Besov space for some , .
4. Comments on Hypotheses
4.1. Asymptotic Normality Results
4.2. Confidence Interval
5. Application to Multivariate Mode Estimation
- 1.
- Long memory discrete time processes: Consider a white noise sequence, , with the variance , and let I and B denote the identity and backshift operators, respectively. As established in [72] (Theorem 1, p. 55), the k-factor Gegenbauer process is given byHowever, [73] demonstrated that when follows a Gaussian distribution, the process is not strongly mixing. Despite this, the moving average formulation guarantees that the process remains stationary, Gaussian, and ergodic. This example underscores the nuanced role of mixing conditions and highlights the significance of the moving average representation in understanding long-term dependencies.
- 2.
- The stationary solution of a linear Markov process: Consider the autoregressive process defined by , where are independent symmetric Bernoulli variables taking the values and 1. As established in [35], this process does not satisfy the α-mixing property due to its intrinsic dependency structure. Nonetheless, it retains key statistical properties such as stationarity, Markovianity, and ergodicity. This example illustrates that a process can be Markovian and ergodic without necessarily being strongly mixing, which has important implications for statistical inference in time series and functional data analysis.
- 3.
- A stationary process with an representation: Consider an independent and identically distributed (i.i.d.) sequence, , uniformly distributed over , and define the process as
- Deterministic sampling.
- Suppose the observation times are irregularly spaced but deterministic and satisfy
- Random sampling.
- Assume the sampling times are independent random variables uniformly distributed on , independent of the process . Denote by
6. Concluding Remarks
7. Proofs
- (a)
- Lyapunov condition:
- (b)
- Lindberg condition:
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Besov Spaces
References
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Didi, S.; Bouzebda, S. Linear Wavelet-Based Estimators of Partial Derivatives of Multivariate Density Function for Stationary and Ergodic Continuous Time Processes. Entropy 2025, 27, 389. https://doi.org/10.3390/e27040389
Didi S, Bouzebda S. Linear Wavelet-Based Estimators of Partial Derivatives of Multivariate Density Function for Stationary and Ergodic Continuous Time Processes. Entropy. 2025; 27(4):389. https://doi.org/10.3390/e27040389
Chicago/Turabian StyleDidi, Sultana, and Salim Bouzebda. 2025. "Linear Wavelet-Based Estimators of Partial Derivatives of Multivariate Density Function for Stationary and Ergodic Continuous Time Processes" Entropy 27, no. 4: 389. https://doi.org/10.3390/e27040389
APA StyleDidi, S., & Bouzebda, S. (2025). Linear Wavelet-Based Estimators of Partial Derivatives of Multivariate Density Function for Stationary and Ergodic Continuous Time Processes. Entropy, 27(4), 389. https://doi.org/10.3390/e27040389