Sieve Inference on Semi-nonparametric Time Series Models

The method of sieves has been widely used in estimating semiparametric and nonparametric models. In this paper, we first provide a general theory on the asymptotic normality of plug-in sieve M estimators of possibly irregular functionals of semi/nonparametric time series models. Next, we establish a surprising result that the asymptotic variances of plug-in sieve M estimators of irregular (i.e., slower than root-T estimable) functionals do not depend on temporal dependence. Nevertheless, ignoring the temporal dependence in small samples may not lead to accurate inference. We then propose an easy-to-compute and more accurate inference procedure based on a "pre-asymptotic" sieve variance estimator that captures temporal dependence. We construct a "pre-asymptotic" Wald statistic using an orthonormal series long run variance (OS-LRV) estimator. For sieve M estimators of both regular (i.e., root-T estimable) and irregular functionals, a scaled "pre-asymptotic" Wald statistic is asymptotically F distributed when the series number of terms in the OS-LRV estimator is held fixed. Simulations indicate that our scaled "pre-asymptotic" Wald test with F critical values has more accurate size in finite samples than the usual Wald test with chi-square critical values.Weak dependence, Sieve M estimation, Sieve Riesz representor, Irregular functional, Misspecification, Pre-asymptotic variance, Orthogonal series long run variance estimation, F distribution

Tonal Behavior Analysis of an Adaptive Second-Order Sigma-Delta Modulator

This paper analyzes the tonal behavior of an adaptive second-order sigma-delta modulator, which was developed and published by the same authors. Idle channel tones, caused by non-white quantization error, is not desirable in applications where the human ear is the end receiver. Besides their relatively small magnitude tones in the baseband, most sigma-delta modulators produce high-powered tones near fs/2. It is a more serious problem because the clock noise near fs/2 can couple these tones down into the baseband. Various simulations show that the more randomized nature of the aforementioned adaptive architecture makes it more advantageous in tonal behavior, particularly attractive in that it significantly reduces the dominant tone near fs/2, which can not be reduced by dithering in a standard second order single-bit modulator. With comparison to the standard second-order sigma-delta modulators, the results are illustrated in both frequency and time domains

Throughput capacity of two-hop relay MANETs under finite buffers

Since the seminal work of Grossglauser and Tse [1], the two-hop relay algorithm and its variants have been attractive for mobile ad hoc networks (MANETs) due to their simplicity and efficiency. However, most literature assumed an infinite buffer size for each node, which is obviously not applicable to a realistic MANET. In this paper, we focus on the exact throughput capacity study of two-hop relay MANETs under the practical finite relay buffer scenario. The arrival process and departure process of the relay queue are fully characterized, and an ergodic Markov chain-based framework is also provided. With this framework, we obtain the limiting distribution of the relay queue and derive the throughput capacity under any relay buffer size. Extensive simulation results are provided to validate our theoretical framework and explore the relationship among the throughput capacity, the relay buffer size and the number of nodes

A New Image Segmentation Algorithm and Its Application in Lettuce Object Segmentation

Lettuce image segmentation which based on computer image processing is the premise of non-destructive testing of lettuce quality. The traditional 2-D maximum entropy algorithm has some faults, such as low accuracy of segmentation, slow speed, and poor anti-noise ability. As a result, it leads to the problems of poor image segmentation and low efficiency. An improved 2-D maximum entropy algorithm is presented in this paper. It redistricts segmented regions and furtherly classifies the segmented image pixels with the method of the minimum fuzzy entropy, and reduces the impact of noise points, as a result the image segmentation accuracy is improved. The improved algorithm is used to lettuce object segmentation, and the experimental results show that the improved segmentation algorithm has many advantages compared with the traditional 2-D maximum entropy algorithm, such as less false interference, strong anti-noise ability, good robustness and validity

Sieve Inference on Semi-nonparametric Time Series Models

The method of sieves has been widely used in estimating semiparametric and nonparametric models. In this paper, we first provide a general theory on the asymptotic normality of plug-in sieve M estimators of possibly irregular functionals of semi/nonparametric time series models. Next, we establish a surprising result that the asymptotic variances of plug-in sieve M estimators of irregular (i.e., slower than root-T estimable) functionals do not depend on temporal dependence. Nevertheless, ignoring the temporal dependence in small samples may not lead to accurate inference. We then propose an easy-to-compute and more accurate inference procedure based on a “pre-asymptotic” sieve variance estimator that captures temporal dependence. We construct a “pre-asymptotic” Wald statistic using an orthonormal series long run variance (OS-LRV) estimator. For sieve M estimators of both regular (i.e., root-T estimable) and irregular functionals, a scaled “pre-asymptotic” Wald statistic is asymptotically F distributed when the series number of terms in the OS-LRV estimator is held fixed. Simulations indicate that our scaled “pre-asymptotic” Wald test with F critical values has more accurate size in finite samples than the usual Wald test with chi-square critical values

Sieve inference on semi-nonparametric time series models

The method of sieves has been widely used in estimating semiparametric and nonparametric models. In this paper, we first provide a general theory on the asymptotic normality of plug-in sieve M estimators of possibly irregular functionals of semi/nonparametric time series models. Next, we establish a surprising result that the asymptotic variances of plug-in sieve M estimators of irregular (i.e., slower than root-T estimable) functionals do not depend on temporal dependence. Nevertheless, ignoring the temporal dependence in small samples may not lead to accurate inference. We then propose an easy-to-compute and more accurate inference procedure based on a “pre-asymptotic” sieve variance estimator that captures temporal dependence. We construct a “pre-asymptotic” Wald statistic using an orthonormal series long run variance (OS-LRV) estimator. For sieve M estimators of both regular (i.e., root-T estimable) and irregular functionals, a scaled “pre-asymptotic” Wald statistic is asymptotically F distributed when the series number of terms in the OS-LRV estimator is held fixed. Simulations indicate that our scaled “pre-asymptotic” Wald test with F critical values has more accurate size in finite samples than the usual Wald test with chi-square critical values

Quantum oscillations in adsorption energetics of atomic oxygen on Pb(111) ultrathin films: A density-functional theory study

Using first-principles calculations, we have systematically studied the quantum size effects of ultrathin Pb(111) films on the adsorption energies and diffusion energy barriers of oxygen atoms. For the on-surface adsorption of oxygen atoms at different coverages, all the adsorption energies are found to show bilayer oscillation behaviors. It is also found that the work function of Pb(111) films still keeps the bilayer-oscillation behavior after the adsorption of oxygen atoms, with the values being enlarged by 2.10 to 2.62 eV. For the diffusion and penetration of the adsorbed oxygen atoms, it is found that the most energetically favored paths are the same on different Pb(111) films. And because of the modulation of quantum size effects, the corresponding energy barriers are all oscillating with a bilayer period on different Pb(111) films. Our studies indicate that the quantum size effect in ultrathin metal films can modulate a lot of processes during surface oxidation