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- Kun Ho Kim, Wei Biao Wu
- 2010

The paper considers local linear regression of a time series model with non-stationary regressors and errors. Asymptotic property of the local linear estimator is derived under a new dependence measure of non-stationary time series. We apply the local linear regression method to estimate the ‘‘time-varying’’ coefficients of an economic-causal model for the… (More)

- Kun Ho Kim, Ting Zhang, Wei Biao Wu
- 2014

The paper considers testing parametric assumptions on the conditional mean and variance functions for nonlinear autoregressive models. To this end, we compare the kernel density estimate of the marginal density of the process with a convolution-type density estimate. It is shown that, interestingly, the latter estimate has a parametric ( √ n) rate of… (More)

- A. C. Morris, F. A. Goswitz, K. H. Kim, A. E. McDow, P. A. Aaron, T. R. Barclay
- Medical and biological engineering
- 1974

MULTIPROBE COUNTERS are used on patients to measure the dynamic distribution patterns of radioactive compounds. Counting probes are positioned over the patient and the radioactive tracer is given intravenously. Each probe then senses a varying counting rate, reflecting the activity in the detector's field of view, thus giving the investigator information… (More)

- Kun Ho Kim
- 2011

In this paper, we construct the uniform confidence band (UCB) of a time-varying trend in a partially linear model. A two-stage local linear regression is proposed to estimate the time-varying trend. Based on this estimate, we develop an invariance principle to construct the UCB of the trend function. The proposed methodology is used to estimate the… (More)

Low dimensional embeddings that capture the main variations of interest in collections of data are important for many applications. One way to construct these embeddings is to acquire estimates of similarity from the crowd. However, similarity is a multi-dimensional concept that varies from individual to individual. Existing models for learning embeddings… (More)

The practice of estimating static conditional mean functions from time series data and then using "robust" covariance matrices for inference is examined against alternative approaches. We show that when contemporaneous exogeneity is violated, that the asymptotic bias associated with GLS can actually be less than that of OLS. This result extends to Feasible… (More)

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