Inference in a Model with at Most One Slope-Change Point

Miao, B Q

doi:10.1016/b978-0-12-580205-5.50031-8

Cited by 4 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, some non-parametric methods are applied to detecting change point, such as applying the Wilcoxon rank to detecting change point in [8,9]. Especially, in [8], the author applied the local comparison method, which can be seen in [10]. However, these papers have not considered the strong convergence rate of change-point estimation.…”

Section: Introductionmentioning

confidence: 99%

“…In the case that higher-order moment is finite, for the change point with variance shift, see [6,7]. Furthermore, some non-parametric methods are applied to detecting change point, such as applying the Wilcoxon rank to detecting change point in [8,9]. Especially, in [8], the author applied the local comparison method, which can be seen in [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Consistency of change point estimators for symmetrical stable distribution with parameters shift

Shi

Bai-qi

Chun-lei

2008

Sci. China Ser. A-Math.

View full text Add to dashboard Cite

Assume that the characteristic index α of stable distribution satisfies 1 < α < 2, and that the distribution is symmetrical about its mean. We consider the change point estimators for stable distribution with α or scale parameter β shift. For the one case that mean is a known constant, if α or β changes, then density function will change too. To this end, we suppose the kernel estimation for a change point. For the other case that mean is an unknown constant, we suppose to apply empirical characteristic function to estimate the change-point location. In the two cases, we consider the consistency and strong convergence rate of estimators. Furthermore, we consider the mean shift case. If mean changes, then corresponding characteristic function will change too. To this end, we also apply empirical characteristic function to estimate change point. We obtain the similar convergence rate. Finally, we consider its application on the detection of mean shift in financial market.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Consistency of change point estimators for symmetrical stable distribution with parameters shift

Shi

Bai-qi

Chun-lei

2008

Sci. China Ser. A-Math.

View full text Add to dashboard Cite

show abstract

“…Quandt [8] , and Quandt and Ramsay [9] used least squares method and maximum likelihood method to inference change point in linear models. Chen [10] investigated hypothesis testing and interval estimation of the change point of the location parameter in linear models, and Miao [11] studied the same problems but for slope parameter. By assuming that there is at most one change point and that the distributions of random sequence are independently continuous, Darkhobsky [12] considered testing the existing of a change point of random sequence.…”

Section: Introductionmentioning

confidence: 99%

Inference of change-point in single index models

Cao

Wang

et al. 2008

Sci. China Ser. A-Math.

View full text Add to dashboard Cite

Single index models are widely used in medicine, econometrics and some other fields.In this paper, we consider the inference of a change point problem in single index models. Based on density-weighted average derivative estimation (ADE) method, we propose a statistic to test whether a change point exists or not. The null distribution of the test statistic is obtained using a permutation technique. The permuted statistic is rigorously shown to have the same distribution in the limiting sense under both null and alternative hypotheses. After the null hypothesis of no change point is rejected, an ADE-based estimate of the change point is proposed under assumption that the change point is unique. A simulation study confirms the theoretical results.

show abstract

“…Husková and Sen (1984) proposed rank tests for a change in the parameters of a multiple regression model. Miao (1988) proposed and studied a simple test based on the difference of cumulative sums of the dependent variable, to test for a change in the slope of the simple regression model. Gombay and Horvá th (1994) proved limit theorems for tests based on LS residuals in multiple regression models.…”

mentioning

confidence: 99%