The Experts below are selected from a list of 13437 Experts worldwide ranked by ideXlab platform

Hsi Tien Chen - One of the best experts on this subject based on the ideXlab platform.

  • statistical Testing for assessing the performance of lifetime index of electronic components with exponential distribution
    International Journal of Quality & Reliability Management, 2002
    Co-Authors: Lee-ing Tong, Kuan Shan Chen, Hsi Tien Chen
    Abstract:

    The electronics industry has heavily prioritized enhancing the quality, lifetime and conforming rate (conforming to specifications) of electronic components. Various methods have been developed for assessing quality performance. In practice, process capability indices (PCIs) are used as a means of measuring process potential and performance. Moreover, most PCIs have been developed or investigated under the assumption that electronic components have a lifetime with a normal distribution. However, PCIs for non‐normal distributions have seldom been discussed. Nevertheless, the lifetime of electronic components generally may possess an exponential, gamma or Weibull distribution and so forth. Under an exponential distribution, some properties of the PCIs and their estimators differ from those in a normal distribution. To utilize the PCIs more reasonably and accurately in assessing the lifetime performance of electronic components, this study constructs a uniformly minimum variance unbiased (UMVU) estimator of their lifetime performance index under an exponential distribution. The UMVU estimator of the lifetime performance index is then utilized to develop the Hypothesis Testing Procedure. The purchasers can then employ the Testing Procedure to determine whether the lifetime of the electronic components adheres to the required level. Manufacturers can also utilize this Procedure to enhance process capability.

Rudolf Guilbaud - One of the best experts on this subject based on the ideXlab platform.

  • Testing equivalence between two laboratories or two methods using paired sample analysis and interval Hypothesis Testing
    Analytical and Bioanalytical Chemistry, 2006
    Co-Authors: Shixia Feng, Qiwei Liang, Kirk Newland, R D Kinser, Rudolf Guilbaud
    Abstract:

    A modified interval Hypothesis Testing Procedure based on paired-sample analysis is described, as well as its application in Testing equivalence between two bioanalytical laboratories or two methods. This Testing Procedure has the advantage of reducing the risk of wrongly concluding equivalence when in fact two laboratories or two methods are not equivalent. The advantage of using paired-sample analysis is that the test is less confounded by the intersample variability than unpaired-sample analysis when incurred biological samples with a wide range of concentrations are included in the experiments. Practical aspects including experimental design, sample size calculation and power estimation are also discussed through examples.

Bhargab Chattopadhyay - One of the best experts on this subject based on the ideXlab platform.

  • virtual dimensionality of hyperspectral data use of multiple Hypothesis Testing for controlling type i error
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
    Co-Authors: Vijayashekhar S S, Jignesh S Bhatt, Bhargab Chattopadhyay
    Abstract:

    Estimating the number of materials present in a scene is the fundamental step in many hyperspectral remote sensing applications. The virtual dimensionality (VD) estimates the number of spectrally distinct materials in the hyperspectral data. The VD is generally considered as the number of signal sources under binary Hypothesis, based on the Neyman–Pearson detection criteria. We observe that the Hypothesis Testing Procedure used in many approaches is prone to inflated Type-I (false positive) error. This is due to carrying out the binary Hypothesis test individually on each band image, i.e., more than 200 images in hyperspectral data. In this article, we propose multiple Hypothesis Testing to control the expected proportion of falsely rejected null hypotheses, i.e., false discovery rate (FDR), and in turn, improve the probability of better performance in estimating the VD. To this end, we employ Benjamini and Hochberg Procedure that controls the FDR. We provide multiple Hypothesis Testing-based algorithms to estimate VD wherein the Hypothesis can be formulated according to eigenanalysis, the target specified by statistical approach, and by geometric analysis. The efficacies of the proposed algorithms are evaluated by estimating the number of endmembers for the spectral unmixing application. We conduct experiments on four synthetic hyperspectral data sets at different noise levels as well as on two well-known real hyperspectral datasets. Time complexity and execution time are discussed to study the algorithmic aspects while sensitivity analyses of parameters are carried out for better performance analysis of the proposed approach. We found that the use of multiple Hypothesis Testing improves estimation of number of endmembers in hyperspectral data.

Benedikt M Potscher - One of the best experts on this subject based on the ideXlab platform.

  • can one estimate the conditional distribution of post model selection estimators
    Annals of Statistics, 2006
    Co-Authors: Hannes Leeb, Benedikt M Potscher
    Abstract:

    We consider the problem of estimating the conditional distribution of a post-model-selection estimator where the conditioning is on the selected model. The notion of a post-model-selection estimator here refers to the combined Procedure resulting from first selecting a model (e.g., by a model selection criterion such as AIC or by a Hypothesis Testing Procedure) and then estimating the parameters in the selected model (e.g., by least-squares or maximum likelihood), all based on the same data set. We show that it is impossible to estimate this distribution with reasonable accuracy even asymptotically. In particular, we show that no estimator for this distribution can be uniformly consistent (not even locally). This follows as a corollary to (local) minimax lower bounds on the performance of estimators for this distribution. Similar impossibility results are also obtained for the conditional distribution of linear functions (e.g., predictors) of the post-model-selection estimator.

  • can one estimate the conditional distribution of post model selection estimators
    Research Papers in Economics, 2003
    Co-Authors: Hannes Leeb, Benedikt M Potscher
    Abstract:

    We consider the problem of estimating the conditional distribution of a post-model-selection estimator where the conditioning is on the selected model. The notion of a post-model-selection estimator here refers to the combined Procedure resulting from first selecting a model (e.g., by a model selection criterion like AIC or by a Hypothesis Testing Procedure) and second estimating the parameters in the selected model (e.g., by least-squares or maximum likelihood), all based on the same data set. We show that it is impossible to estimate this distribution with reasonable accuracy even asymptotically. In particular, we show that no estimator for this distribution can be uniformly consistent (not even locally). This follows as a corollary to (local) minimax lower bounds on the performance of estimators for this distribution. Similar impossibility results are also obtained for the conditional distribution of linear functions (e.g., predictors) of the post-model-selection estimator.

Lee-ing Tong - One of the best experts on this subject based on the ideXlab platform.

  • statistical Testing for assessing the performance of lifetime index of electronic components with exponential distribution
    International Journal of Quality & Reliability Management, 2002
    Co-Authors: Lee-ing Tong, Kuan Shan Chen, Hsi Tien Chen
    Abstract:

    The electronics industry has heavily prioritized enhancing the quality, lifetime and conforming rate (conforming to specifications) of electronic components. Various methods have been developed for assessing quality performance. In practice, process capability indices (PCIs) are used as a means of measuring process potential and performance. Moreover, most PCIs have been developed or investigated under the assumption that electronic components have a lifetime with a normal distribution. However, PCIs for non‐normal distributions have seldom been discussed. Nevertheless, the lifetime of electronic components generally may possess an exponential, gamma or Weibull distribution and so forth. Under an exponential distribution, some properties of the PCIs and their estimators differ from those in a normal distribution. To utilize the PCIs more reasonably and accurately in assessing the lifetime performance of electronic components, this study constructs a uniformly minimum variance unbiased (UMVU) estimator of their lifetime performance index under an exponential distribution. The UMVU estimator of the lifetime performance index is then utilized to develop the Hypothesis Testing Procedure. The purchasers can then employ the Testing Procedure to determine whether the lifetime of the electronic components adheres to the required level. Manufacturers can also utilize this Procedure to enhance process capability.