Description: This function calculate the performance, based on Bayes theorem, of a clinical test. The input is based on a 2x2 matrix (true positive, false positives false negative, true negatives).
The Outputs are:
- Prevalence of disease
- Test Sensibility with 95 confidence interval
- Test Specificity with 95 confidence interval
- False positive and negative proportions
- Youden s Index
- Matthews Correlation Coefficient
- Number needed to Diagnose (NDD)
- Discriminant Power
- Test Accuracy
- Mis-classification Rate
- Positive predictivity
- Negative predictivity
- Positive Likelihood Ratio
- Negative Likelihood Ratio
- Test bias
- Diagnostic odds ratio
- Error odds ratio-This function calculate the performance, based on Bayes theorem, of a clinical test. The input is based on a 2x2 matrix (true positive, false positives false negative, true negatives).
The Outputs are:
- Prevalence of disease
- Test Sensibility with 95 confidence interval
- Test Specificity with 95 confidence interval
- False positive and negative proportions
- Youden s Index
- Matthews Correlation Coefficient
- Number needed to Diagnose (NDD)
- Discriminant Power
- Test Accuracy
- Mis-classification Rate
- Positive predictivity
- Negative predictivity
- Positive Likelihood Ratio
- Negative Likelihood Ratio
- Test bias
- Diagnostic odds ratio
- Error odds ratio Platform: |
Size: 3072 |
Author:Rafal |
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Description: 自动谱分析：可用于丢失/采样/子束光谱分析；矢量自动迭代，可用于建模，故障诊断；-The applications of this additional toolbox are:
- Automatic spectral analysis for Irregular sampling/Missing data, analysis of spectral subbands,
- Vector Autoregressive modeling and Detection [uses ARMASA]
- Reduced statistics ARMAsel: A compact yet accurate ARMA model is obtained based on a given power spectrum. Can be used for generation of colored noise with a prescribed spectrum.
- ARfil algorithm: The analysis of missing data/irregularly sampled signals
- Subband analysis: Accurate analysis of a part of the power spectrum
- Detection: Generally applicable test statistic to determine whether two signals have been generated by the same process or not. Based on the Kullback-Leibler index or Likelihood Ratio.
- Analysis of segments of data, possibly of unequal length. Platform: |
Size: 302080 |
Author:王佳 |
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Description: This paper presents an on-line Statistical Process Control (SPC) technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in autocorrelated processes that follow a normally distributed Autoregressive Integrated Moving Average (ARIMA) model. The GLRT is applied to the uncorrelated residuals of the appropriate time-series model. The performance of the GLRT is compared to two other commonly applied residual-based tests – a Shewhart individuals chart and a CUSUM test. A wide range of ARIMA models are considered, with the conclusion that the best residual-based test to use depends on the particular ARIMA model used to describe the autocorrelation. For many models, the Platform: |
Size: 28672 |
Author:sakthivel |
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Description: 图像的变化通常没有先验信息，或者即便有也很难用参数模型描述它，当两者都知道时，我们可以得到分布的似然比，构造比检验。-Image changes often do not have a priori information, or even if there is difficult to describe it with the parameter model, when both are known, we can get the distribution of the likelihood ratio to construct ratio test. Platform: |
Size: 1024 |
Author:zhangping |
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Description: This code performs likelihood ratio test (LRT) for VAR model to determine optimal lag length,
constant term is automatically included.
Test is performed on contiguous lags, thus DOF=neqn^2*1. Guido Travaglini, 06.15.2011
- This code performs likelihood ratio test (LRT) for VAR model to determine optimal lag length,
constant term is automatically included.
Test is performed on contiguous lags, thus DOF=neqn^2*1. Guido Travaglini, 06.15.2011
Platform: |
Size: 1024 |
Author:jay |
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Description: 本程序是用似然比检验的方法进行profile中的变点检测-This procedure is used likelihood ratio test method of change point detection profile Platform: |
Size: 2048 |
Author:Eric |
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Description: This is a GAUSS program. It will implement the estimation and testing
procedures for a Markov switching parameter model as presented in B. Hansen
"The likelihood ratio test under non-standard conditions: Testing the
Markov trend model of GNP."
-This is a GAUSS program. It will implement the estimation and testing
procedures for a Markov switching parameter model as presented in B. Hansen
"The likelihood ratio test under non-standard conditions: Testing the
Markov trend model of GNP."
Platform: |
Size: 9216 |
Author:Aviral Kumar Tiwari |
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Description: This is a matlab program. It will implement the estimation and testing
procedures for a Markov switching parameter model as presented in B. Hansen
"The likelihood ratio test under non-standard conditions: Testing the
Markov trend model of GNP."
-This is a matlab program. It will implement the estimation and testing
procedures for a Markov switching parameter model as presented in B. Hansen
"The likelihood ratio test under non-standard conditions: Testing the
Markov trend model of GNP."
Platform: |
Size: 10240 |
Author:Aviral Kumar Tiwari |
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Description: Abstract—In slow frequency hopping systems, it is critical
to detect, classify, and mitigate interference, as it degrades the
system performance. Generalized likelihood ratio test algorithm
was proposed for the detection of interference. However, it could
not classify the type of interference. In this paper, we propose
algorithms to classify between follower/stationary interference
and multitone interference/partial-band noise interference. Classification
of interference in various types of modulation schemes
is performed based on a ratio-testing approach, where the ratio
is compared with a predetermined threshold value. It is critical
to set the threshold at an optimal value in order to achieve a good
classification performance. The optimal value and classification
performance are obtained through simulation results. Platform: |
Size: 139264 |
Author:ppdghius |
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Description: 本设计主要对4PSK调制方式的信号，利用MATLAB的m文件进行最佳接收机的设计与仿真。对输入的叠加噪声的4PSK调制信号进行接收，利用相关解调器来实现信号解调，及最大似然准则来实现检测器。在相关解调器中，接收信号分别与基函数 和 相乘再积分。在检测器中，利用相位来判断输出，从而最终得到接收的数据。采用随机二进制数通过4PSK调制后叠加高斯白噪声再对设计的接收机进行测试，从测试的结果可看出，在信噪比大于-8dB时，误码率为0，说明该接收机较好的实现了抗噪声性能。-The design of the main 4PSK modulation mode of the signal, the use of MATLAB m file for the best receiver design and simulation. The 4PSK modulated signal of the input superimposed noise is received, and the demodulator is realized by the relevant demodulator, and the maximum likelihood criterion is used to realize the detector. In the associated demodulator, the received signal is multiplied by the basis function and multiplied by the basis function. In the detector, the output is judged by the phase, and finally the received data is obtained. Using the random binary number through 4PSK modulation after the superposition of white noise and then the design of the receiver to test the test results can be seen in the signal to noise ratio greater than-8dB, the bit error rate is 0, indicating that the receiver is better The realization of the anti-noise performance. Platform: |
Size: 21504 |
Author:kangyuxiang |
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Description: 前已提出的频谱感知方法主要包括匹配滤波器检测、 能量检测、 循环平稳特征检测以及多分辨率频谱感知. 这些方法均为单节点感知方法.然而，在阴影和深度衰落情况下， 单个节点的感知结果并不可靠， 因此， 需要对多个节点的感知结果进行融合，以提高检测可靠性， 即协作感知技术. 文献采用“或” 准则对各个 CR 感知结果进行融合. 文献则提出了基于 D-S 证据理论的协作频谱感知算法，虽然该算法的性能比“或” 准则或“与”准则要好， 但需要存储大量历史信息， 算法的计算复杂度也很高. 文献中分析了采用似然比检测(likelihood ratio test， LRT) 的软判决与采用“与” 准则的硬判决的性能， 结果表明采用软判决的协作感知性能更优(Previously proposed spectrum sensing methods mainly include matched filter detection, energy detection, cyclostationary feature detection and multi-resolution spectrum sensing. These methods are all single node sensing methods. However, in the case of shadow and deep fading, the sensing results of single node are not reliable, so, It is necessary to fuse the sensing results of multiple nodes to improve the detection reliability, i.e. cooperative sensing technology. In the literature, "or" criterion is used to fuse the CR sensing results. In the literature, a cooperative spectrum sensing algorithm based on D-S evidence theory is proposed. Although the performance of the algorithm is better than "or" criterion or "and" criterion, a large amount of historical information needs to be stored, The computational complexity of the algorithm is also very high. In the literature, the performance of the soft decision based on the likelihood ratio test) Platform: |
Size: 6144 |
Author:UU仔 |
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Search - likelihood ratio test