13 results on '"Gemai Chen"'
Search Results
2. Percentage points and power of a K-S type test for linearity in autoregressive time series
- Author
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Gemai, Chen, Min, Chen, and Guofu, Wu
- Published
- 2001
- Full Text
- View/download PDF
3. On t and EWMA t Charts for Monitoring Changes in the Process Mean
- Author
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Gemai Chen, Lingyun Zhang, Philippe Castagliola, Institut de Recherche en Communications et en Cybernétique de Nantes (IRCCyN), Mines Nantes (Mines Nantes)-École Centrale de Nantes (ECN)-Ecole Polytechnique de l'Université de Nantes (EPUN), and Université de Nantes (UN)-Université de Nantes (UN)-PRES Université Nantes Angers Le Mans (UNAM)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Engineering ,021103 operations research ,business.industry ,X-bar chart ,0211 other engineering and technologies ,02 engineering and technology ,Management Science and Operations Research ,01 natural sciences ,\bar x and s chart ,Standard deviation ,010104 statistics & probability ,Chart ,Control limits ,Statistics ,EWMA chart ,0101 mathematics ,Safety, Risk, Reliability and Quality ,Shewhart individuals control chart ,business ,\bar x and R chart ,ComputingMilieux_MISCELLANEOUS - Abstract
The performance of an X-bar chart is usually studied under the assumption that the process standard deviation is well estimated and does not change. This is, of course, not always the case in practice. We find that X-bar charts are not robust against errors in estimating the process standard deviation or changing standard deviation. In this paper we discuss the use of a t chart and an exponentially weighted moving average (EWMA) t chart to monitor the process mean. We determine the optimal control limits for the EWMA t chart and show that this chart has the desired robustness property. Copyright © 2009 John Wiley & Sons, Ltd.
- Published
- 2009
4. The Exact Run Length Distribution and Design of the S2 Chart when the In-Control Variance is Estimated
- Author
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Gemai Chen, Philippe Castagliola, Giovanni Celano, Institut de Recherche en Communications et en Cybernétique de Nantes (IRCCyN), Mines Nantes (Mines Nantes)-École Centrale de Nantes (ECN)-Ecole Polytechnique de l'Université de Nantes (EPUN), and Université de Nantes (UN)-Université de Nantes (UN)-PRES Université Nantes Angers Le Mans (UNAM)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
General Computer Science ,X-bar chart ,Energy Engineering and Power Technology ,Aerospace Engineering ,s2 control chart ,Variance (accounting) ,Conditional probability distribution ,Industrial and Manufacturing Engineering ,Standard deviation ,Process variance ,conditional distribution ,One-way analysis of variance ,Nuclear Energy and Engineering ,Chart ,Control limits ,Statistics ,Control chart ,Electrical and Electronic Engineering ,Safety, Risk, Reliability and Quality ,ComputingMilieux_MISCELLANEOUS ,Mathematics - Abstract
When monitoring the process variability, it is a common practice that a Phase I data set is used to estimate the unknown in-control process standard deviation σ0 or variance [Formula: see text] to set up the control limits, then monitoring proceeds. Once the process is considered to be in-control, the estimated control limits are assumed as fixed. This practice ignores the effect of estimating the unknown in-control process variance [Formula: see text]. In this paper, we derive the exact run length distribution of the S2 control chart when the in-control process variance [Formula: see text] is estimated and find that m = 200 or more Phase I samples are needed to neglect the effect of using estimated control limits. New control limits when m is small are also derived.
- Published
- 2009
5. Weighted profile least squares estimation for a panel data varying-coefficient partially linear model.
- Author
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Bin ZHOU, Jinhong YOU, Qinfeng XU, and Gemai CHEN
- Subjects
LEAST squares ,LINEAR statistical models ,MATHEMATICAL models ,ESTIMATION theory ,STATISTICAL bootstrapping ,POLYNOMIALS - Abstract
This paper is concerned with inference of panel data varying-coefficient partially linear models with a one-way error structure. The model is a natural extension of the well-known panel data linear model (due to Baltagi 1995) to the setting of semiparametric regressions. The authors propose a weighted profile least squares estimator (WPLSE) and a weighted local polynomial estimator (WLPE) for the parametric and nonparametric components, respectively. It is shown that the WPLSE is asymptotically more efficient than the usual profile least squares estimator (PLSE), and that the WLPE is also asymptotically more efficient than the usual local polynomial estimator (LPE). The latter is an interesting result. According to Ruckstuhl, Welsh and Carroll (2000) and Lin and Carroll (2000), ignoring the correlation structure entirely and "pretending" that the data are really independent will result in more efficient estimators when estimating nonparametric regression with longitudinal or panel data. The result in this paper shows that this is not true when the design points of the nonparametric component have a closeness property within groups. The asymptotic properties of the proposed weighted estimators are derived. In addition, a block bootstrap test is proposed for the goodness of fit of models, which can accommodate the correlations within groups. Some simulation studies are conducted to illustrate the finite sample performances of the proposed procedures. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
6. Iterative Weighted Semiparametric Least Squares Estimation in Repeated Measurement Partially Linear Regression Models.
- Author
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Gemai Chen and Jin-hong You
- Abstract
Abstract Consider a repeated measurement partially linear regression model with an unknown vector parameter ß1, an unknown function g(·), and unknown heteroscedastic error variances. In order to improve the semiparametric generalized least squares estimator (SGLSE) of ß, we propose an iterative weighted semiparametric least squares estimator (IWSLSE) and show that it improves upon the SGLSE in terms of asymptotic covariance matrix. An adaptive procedure is given to determine the number of iterations. We also show that when the number of replicates is less than or equal to two, the IWSLSE can not improve upon the SGLSE. These results are generalizations of those in [2] to the case of semiparametric regressions. [ABSTRACT FROM AUTHOR]
- Published
- 2005
7. An asymptotic theory for semiparametric generalized least squares estimation in partially linear regression models.
- Author
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Gemai Chen and Jinhong You
- Subjects
LEAST squares ,ESTIMATION theory ,REGRESSION analysis ,HETEROSCEDASTICITY ,MATHEMATICAL statistics - Abstract
Consider a partially linear regression model with an unknown vector parameter Β, an unknown function g(·), and unknown heteroscedastic error variances. In this paper we develop an asymptotic semiparametric generalized least squares estimation theory under some weak moment conditions. These moment conditions are satisfied by many of the error distributions encountered in practice, and our theory does not require the number of replications to go to infinity. [ABSTRACT FROM AUTHOR]
- Published
- 2005
- Full Text
- View/download PDF
8. Millennial-scale relationships of diatom species richness and production in two prairie lakes.
- Author
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Rusak, James A., Leavitt, Peter R., McGowan, Suzanne, Gemai Chen, Olson, Olaf, Wunsam, Sybille, and Cumming, Brian F.
- Subjects
BIODIVERSITY ,SPECIES ,BIOTIC communities ,LAKES ,DIATOMS - Abstract
Insight into the causes and consequences of changes in aquatic biodiversity requires an improved understanding of the nature of the relationships between species richness and ecosystem function over a much longer temporal perspective than we currently possess. We used high-resolution paleoecological records from two prairie lakes to show that diatom species richness (as fossil frustules) was negatively correlated (r² = 0.09-0.24, p < 0.001) with diatom production (as fossil pigments) during the past 2,000 yr. By comparing analyses from intervals of fresh and saline waters, we demonstrate that these significant richness-production relationships arose during freshwater periods (r² = 0.13-0.45, p < 0.001) and could be eliminated (r² < 0.02, p > 0.1) by abiotic disturbances such as droughts. Procrustes analyses of the concordance of species change within freshwater communities and the change in richness--production relationships through time revealed that shifts in diatom community composition could have a large influence in determining the negative relationship between richness and production. Finally, significant correlations (r² = 0.09-0.24, p < 0.0001) between past diatom species richness and ratios of stable isotopes (primarily δ
15 N) suggested that C and N biogeochemical cycles are also linked to changes in algal biodiversity. Taken together, these analyses suggest that the ongoing disruption of climate and biogeochemical systems by humans may obscure the relationship between aquatic biodiversity and ecosystem function in the future. [ABSTRACT FROM AUTHOR]- Published
- 2004
- Full Text
- View/download PDF
9. EWMA Charts for Monitoring the Mean of Censored Weibull Lifetimes.
- Author
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Lingyun Zhang and Gemai Chen
- Subjects
WEIBULL distribution ,DISTRIBUTION (Probability theory) ,ARITHMETIC mean ,PROCESS control systems ,QUALITY control charts - Abstract
A lower-sided and an upper-sided exponentially weighted moving average (EWMA) chart for detecting mean changes (decreases and increases) in processes characterized by Weibull distributions are developed in this paper when censoring occurs at a fixed level. We show that the lower-sided EWMA chart performs better than its counterpart, the Shewhart-type; that a Shewhart-type chart is not appropriate to detect mean increases when the censoring rate is high; and that the upper-sided EWMA chart works well in detecting mean increases. [ABSTRACT FROM AUTHOR]
- Published
- 2004
- Full Text
- View/download PDF
10. Delete-group Jackknife Estimate in Partially Linear Regression Models with Heteroscedasticity.
- Author
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Jin-hong You and Gemai Chen
- Subjects
REGRESSION analysis ,ANALYSIS of covariance - Abstract
Consider a partially linear regression model with an unknown vector parameter ß, an unknown function g(·), and unknown heteroscedastic error variances. Chen, You
[23] proposed a semiparametric generalized least squares estimator (SGLSE) for ß, which takes the heteroscedasticity into account to increase efficiency. For inference based on this SGLSE, it is necessary to construct a consistent estimator for its asymptotic covariance matrix. However, when there exists within-group correlation, the traditional delta method and the delete-1 jackknife estimation fail to offer such a consistent estimator. In this paper, by deleting grouped partial residuals a delete-group jackknife method is examined. It is shown that the delete-group jackknife method indeed can provide a consistent estimator for the asymptotic covariance matrix in the presence of within-group correlations. This result is an extension of that in [21]. [ABSTRACT FROM AUTHOR]- Published
- 2003
11. Uniform Convergence Rate of Estimators of Autocovariances in Partly Linear Regression Models with Correlated Errors.
- Author
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Jin-hong You, Gemai Chen, Min Chen, and Xue-lei Jiang
- Subjects
REGRESSION analysis ,MATHEMATICAL analysis ,MATHEMATICAL statistics - Abstract
Consider the partly linear regression model % MathType!Translator!2!1!AMS LaTeX.tdl!TeX -- AMS-LaTeX! % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D % aebbnrfifHhDYfgasaacH8srps0lbbf9q8WrFfeuY-Hhbbf9v8qqaq % Fr0xc9pk0xbba9q8WqFfea0-yr0RYxir-Jbba9q8aq0-yq-He9q8qq % Q8frFve9Fve9Ff0dmeaabaqaciGacaGaaeqabaWaaeaaeaaakeaaca % WG5bWaaSbaaSqaaiaadMgaaeqaaOGaeyypa0JabmiEayaafaWaaSba % aSqaaiaadMgaaeqaaOGaeqOSdiMaey4kaSIaam4zamaabmaabaGaam % iDamaaBaaaleaacaWGPbaabeaaaOGaayjkaiaawMcaaiabgUcaRiab % ew7aLnaaBaaaleaacaWGPbaabeaakiaacYcacaaMe8UaaGjbVlaayI % W7caaIXaGaeyizImQaamyAaiabgsMiJkaad6gaaaa!53DA! $$ y_{i} = {x}''_{i} \beta + g{\left( {t_{i} } \right)} + \varepsilon _{i} ,\;\;{\kern 1pt} 1 \leqslant i \leqslant n $$ , where y
i ?s are responses, % MathType!Translator!2!1!AMS LaTeX.tdl!TeX -- AMS-LaTeX! % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D % aebbnrfifHhDYfgasaacH8srps0lbbf9q8WrFfeuY-Hhbbf9v8qqaq % Fr0xc9pk0xbba9q8WqFfea0-yr0RYxir-Jbba9q8aq0-yq-He9q8qq % Q8frFve9Fve9Ff0dmeaabaqaciGacaGaaeqabaWaaeaaeaaakeaaca % WG4bWaaSbaaSqaaiaadMgaaeqaaOGaeyypa0ZaaeWaaeaacaWG4bWa % aSbaaSqaaiaadMgacaaIXaaabeaakiaacYcacaWG4bWaaSbaaSqaai % aadMgacaaIYaaabeaakiaacYcacqWIVlctcaGGSaGaamiEamaaBaaa % leaacaWGPbGaamiCaaqabaaakiaawIcacaGLPaaadaahaaWcbeqaaO % Gamai4gkdiIcaacaaMe8UaaGjbVlaaysW7caqGHbGaaeOBaiaabsga % caaMe8UaaGjbVlaaysW7caWG0bWaaSbaaSqaaiaadMgaaeqaaOGaey % icI4Saamivaaaa!5D82! $$ x_{i} = {\left( {x_{{i1}} ,x_{{i2}} , \cdots ,x_{{ip}} } \right)}^{\prime } \;\;\;{\text{and}}\;\;\;t_{i} \in {\cal T} $$ are known and nonrandom design points, % MathType!Translator!2!1!AMS LaTeX.tdl!TeX -- AMS-LaTeX! % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D % aebbnrfifHhDYfgasaacH8srps0lbbf9q8WrFfeuY-Hhbbf9v8qqaq % Fr0xc9pk0xbba9q8WqFfea0-yr0RYxir-Jbba9q8aq0-yq-He9q8qq % Q8frFve9Fve9Ff0dmeaabaqaciGacaGaaeqabaWaaeaaeaaakeaaca % WGubaaaa!393B! $$ {\cal T} $$ is a compact set in the real line % MathType!Translator!2!1!AMS LaTeX.tdl!TeX -- AMS-LaTeX! % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D % aebbnrfifHhDYfgasaacH8srps0lbbf9q8WrFfeuY-Hhbbf9v8qqaq % Fr0xc9pk0xbba9q8WqFfea0-yr0RYxir-Jbba9q8aq0-yq-He9q8qq % Q8frFve9Fve9Ff0dmeaabaqaciGacaGaaeqabaWaaeaaeaaakeaaca % WGsbaaaa!3939! $$ {\cal R} $$ , ß = ( ß1 , ··· , ßp )? is an unknown parameter vector, g(·) is an unknown function and { ei } is a linear process, i.e., % MathType!Translator!2!1!AMS LaTeX.tdl!TeX -- AMS-LaTeX! % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D % aebbnrfifHhDYfgasaacH8srps0lbbf9q8WrFfeuY-Hhbbf9v8qqaq % Fr0xc9pk0xbba9q8WqFfea0-yr0RYxir-Jbba9q8aq0-yq-He9q8qq % Q8frFve9Fve9Ff0dmeaabaqaciGacaGaaeqabaWaaeaaeaaakeaacq % aH1oqzdaWgaaWcbaGaamyAaaqabaGccaaMi8Uaeyypa0JaaGjcVpaa % qahabaGaeqiYdK3aaSbaaSqaaiaadQgaaeqaaOGaamyzamaaBaaale % aacaWGPbGaeyOeI0IaamOAaaqabaGccaGGSaGaaGjcVlaaysW7cqaH % ipqEdaWgaaWcbaGaaGimaaqabaGccaaMi8Uaeyypa0JaaGjcVlaaig % dacaGGSaGaaGjbVlaayIW7daaeWbqaamaaemaabaGaeqiYdK3aaSba % aSqaaiaadQgaaeqaaaGccaGLhWUaayjcSdGaeyipaWJaeyOhIukale % aacaWGQbGaeyypa0JaaGimaaqaaiabg6HiLcqdcqGHris5aaWcbaGa % amOAaiabg2da9iaaicdaaeaacqGHEisPa0GaeyyeIuoaaaa!6ABD! $$ \varepsilon _{i} {\kern 1pt} = {\kern 1pt} {\sum\limits_{j = 0}^\infty {\psi _{j} e_{{i - j}} ,{\kern 1pt} \;\psi _{0} {\kern 1pt} = {\kern 1pt} 1,\;{\kern 1pt} {\sum\limits_{j = 0}^\infty {{\left| {\psi _{j} } \right|} < \infty } }} } $$ , where ej are i.i.d. random variables with zero mean and variance % MathType!Translator!2!1!AMS LaTeX.tdl!TeX -- AMS-LaTeX! % MathType!MTEF!2!1!+- % feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D % aebbnrfifHhDYfgasaacH8srps0lbbf9q8WrFfeuY-Hhbbf9v8qqaq % Fr0xc9pk0xbba9q8WqFfea0-yr0RYxir-Jbba9q8aq0-yq-He9q8qq % Q8frFve9Fve9Ff0dmeaabaqaciGacaGaaeqabaWaaeaaeaaakeaacq % aHdpWCdaqhaaWcbaGaamyzaaqaaiaaikdaaaaaaa!3BF8! $$ \sigma ^{2}_{e} $$ . Drawing upon B-spline estimation of g(·) and least squares estimation of ß, we construct estimators of the autocovariances of { ei }. The uniform strong convergence rate of these estimators to their true values is then established. These results not only are a compensation for those of [23], but also have some application in modeling error structure. When the errors { ei } are an ARMA process, our result can be used to develop a consistent procedure for determining the order of the ARMA process and identifying the non-zero coeffcients of the process. Moreover, our result can be used to construct the asymptotically effcient estimators for parameters in the ARMA error process. [ABSTRACT FROM AUTHOR]- Published
- 2003
12. An improved p chart through simple adjustments.
- Author
-
Gemai Chen
- Subjects
FRACTIONS - Abstract
Proposes a chart for an improved monitoring of the p fraction. Two alternatives to the p chart; Guidelines for choosing an appropriate chart; Advantages of simple adjustments; Explanation of T.P. Ryan's rule of thumb.
- Published
- 1998
- Full Text
- View/download PDF
13. Jackknifing in partially linear regression models with serially correlated errors
- Author
-
Xian Zhou, Jinhong You, and Gemai Chen
- Subjects
Statistics and Probability ,Jackknife-type estimation ,Numerical Analysis ,Statistics::Theory ,Mean squared error ,Semiparametric least-squares estimator ,Coverage probability ,Estimator ,Serially correlated errors ,Delta method ,Minimum-variance unbiased estimator ,Efficient estimator ,Consistent estimator ,Statistics ,Partially linear regression model ,Statistics::Methodology ,Asymptotic normality ,Consistency ,Statistics, Probability and Uncertainty ,Robustness ,Jackknife resampling ,Mathematics - Abstract
In this paper jackknifing technique is examined for functions of the parametric component in a partially linear regression model with serially correlated errors. By deleting partial residuals a jackknife-type estimator is proposed. It is shown that the jackknife-type estimator and the usual semiparametric least-squares estimator (SLSE) are asymptotically equivalent. However, simulation shows that the former has smaller biases than the latter when the sample size is small or moderate. Moreover, since the errors are correlated, both the Tukey type and the delta type jackknife asymptotic variance estimators are not consistent. By introducing cross-product terms, a consistent estimator of the jackknife asymptotic variance is constructed and shown to be robust against heterogeneity of the error variances. In addition, simulation results show that confidence interval estimation based on the proposed jackknife estimator has better coverage probability than that based on the SLSE, even though the latter uses the information of the error structure, while the former does not.
- Full Text
- View/download PDF
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