A new dual-mode blind equalization based on estimated error variance

2006 ◽  
Author(s):  
Lanqing Sun ◽  
Lindong Ge ◽  
Feng Liu
Keyword(s):  
Blind Equalization ◽  
Error Variance ◽  
Dual Mode ◽  
Estimated Error

scholarly journals Estimation of Error Variance-Covariance Parameters Using Multivariate Geographically Weighted Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-5 ◽  
Author(s):  
Sri Harini
Keyword(s):  
Statistical Inference ◽  
Geographically Weighted Regression ◽  
Weighting Function ◽  
Likelihood Estimation ◽  
Error Variance ◽  
Least Square ◽  
Weighted Regression ◽  
Inference Procedure ◽  
Geographically Weighted Regression Model ◽  
Estimated Error

The Multivariate Geographically Weighted Regression (MGWR) model is a development of the Geographically Weighted Regression (GWR) model that takes into account spatial heterogeneity and autocorrelation error factors that are localized at each observation location. The MGWR model is assumed to be an error vector ε that distributed as a multivariate normally with zero vector mean and variance-covariance matrix Σ at each location ui,vi, which Σ is sized qxq for samples at the i-location. In this study, the estimated error variance-covariance parameters is obtained from the MGWR model using Maximum Likelihood Estimation (MLE) and Weighted Least Square (WLS) methods. The selection of the WLS method is based on the weighting function measured from the standard deviation of the distance vector between one observation location and another observation location. This test uses a statistical inference procedure by reducing the MGWR model equation so that the estimated error variance-covariance parameters meet the characteristics of unbiased. This study also provides researchers with an understanding of statistical inference procedures.

scholarly journals Estimating observation and model error variances using multiple data sets

2018 ◽  
Vol 11 (7) ◽  
pp. 4239-4260 ◽  
Author(s):  
Richard Anthes ◽  
Therese Rieckh
Keyword(s):  
Error Variance ◽  
Specific Humidity ◽  
Data Sets ◽  
Data Set ◽  
Multiple Data ◽  
Gfs Model ◽  
Multiple Data Sets ◽  
Using Data ◽  
The Tropics ◽  
Estimated Error

Abstract. In this paper we show how multiple data sets, including observations and models, can be combined using the “three-cornered hat” (3CH) method to estimate vertical profiles of the errors of each system. Using data from 2007, we estimate the error variances of radio occultation (RO), radiosondes, ERA-Interim, and Global Forecast System (GFS) model data sets at four radiosonde locations in the tropics and subtropics. A key assumption is the neglect of error covariances among the different data sets, and we examine the consequences of this assumption on the resulting error estimates. Our results show that different combinations of the four data sets yield similar relative and specific humidity, temperature, and refractivity error variance profiles at the four stations, and these estimates are consistent with previous estimates where available. These results thus indicate that the correlations of the errors among all data sets are small and the 3CH method yields realistic error variance profiles. The estimated error variances of the ERA-Interim data set are smallest, a reasonable result considering the excellent model and data assimilation system and assimilation of high-quality observations. For the four locations studied, RO has smaller error variances than radiosondes, in agreement with previous studies. Part of the larger error variance of the radiosondes is associated with representativeness differences because radiosondes are point measurements, while the other data sets represent horizontal averages over scales of ∼ 100 km.

The effects of some variables on the components of reproduction rate in the Merino

1969 ◽  
Vol 20 (3) ◽  
pp. 549 ◽  
Author(s):  
Haas HJ De ◽  
AA Dunlop
Keyword(s):  
Body Weight ◽  
Error Variance ◽  
The Other ◽  
Multiple Births ◽  
Multiple Birth ◽  
Reproduction Rate ◽  
Components Of Variance ◽  
Estimated Error ◽  
Main Effects ◽  
Single Birth

Reproductive records covering 4855 ewe-years coming from five strains of Merino ewe run at three locations over 5 years were classified into those which resulted in (a) failure to lamb, (b) a single birth, or (c) a multiple birth. Age of ewe was included as a further classification, while pre-mating body weight was considered as a covariate. The data were analysed by least squares procedures. In all analyses in which components of variance were estimated, error variance constituted more than 90% of the total. Of the main effects, those due to age were generally largest, particularly where they related to the proportion of dry ewes and multiple births, though year effects on the proportion of dry ewes ranged up to 0.10. The effects of pre-mating body weight on lambing performance were small though real, the largest being an increase of 0.37% of multiple births per pound increase in body weight. First order interactions were generally small, the most prominent being location x strain, location x age, and location x year. The third of these had the largest effects and accounted for more of the variance. This was particularly so in the proportions of dry ewes and single births. Location x age interactions, on the other hand, were more prominent in affecting the proportion of multiple births, where the increase with age was much less marked at one location than at the other two. Strain x location interactions were not large enough to suggest any marked adaptation of strains to particular locations in these mutually dependent traits.

scholarly journals A New Method for Temperature Spatial Interpolation Based on Sparse Historical Stations

2018 ◽  
Vol 31 (5) ◽  
pp. 1757-1770 ◽  
Author(s):  
Chengdong Xu ◽  
Jinfeng Wang ◽  
Qingxiang Li
Keyword(s):  
Error Variance ◽  
Absolute Error ◽  
Historical Period ◽  
Climate Change Research ◽  
Air Temperatures ◽  
Point Interpolation ◽  
Historical Temperature ◽  
Leave One Out ◽  
Variance Estimates ◽  
Estimated Error

Long-term grid historical temperature datasets are the foundation of climate change research. Datasets developed by traditional interpolation methods usually contain data for a period of less than 50 yr, with a relatively low spatial resolution owing to the sparse distribution of stations in the historical period. In this study, the point interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (P-BSHADE) method has been used to interpolate 1-km grids of monthly surface air temperatures in the historical period of 1900–50 in China. The method can be used to remedy the station bias resulting from sparse coverage, and it considers the characteristics of spatial autocorrelation and nonhomogeneity of the temperature distribution to obtain unbiased and minimum error variance estimates. The results have been compared with those from widely used methods such as kriging, inverse distance weighting (IDW), and a combined spline with kriging (TPS-KRG) method, both theoretically and empirically. The leave-one-out cross-validation method using a real dataset was implemented. The root-mean-square error (RMSE) [mean absolute error (MAE)] for P-BSHADE is 0.98°C (0.75°C), while those for TPS-KRG, kriging, and IDW are 1.46° (1.07°), 2.23° (1.51°), and 2.64°C (1.85°C), respectively. The results of validation using a simulated dataset also present the smallest error for P-BSHADE, demonstrating its empirical superiority. In addition to its empirical superiority, the method also can produce a map of the estimated error variance, representing the uncertainty of estimation.

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