For this purpose numerous cost estimation techniques have been developed during the past years.
There are numerous volume estimation methods, but unfortunately this is commonly not provided or clearly described in the source literature.
There are numerous blind estimation and equalization techniques available in the literature, namely, subspace-based methods [10, 11], second-order statistics [12], Cholesky factorization [13], and iterative methods [14].
Our works offers a more comprehensive theory focused on FL distributions, including numerous new results, estimation, and data examples.
In addition, we made numerous spatial weighted estimation assumptions.
Numerous moment-based estimation techniques, such as the very popular DerSimonian-Laird [ 6, 27] and Mandel-Paule [ 28, 29] methods use the first moment (K−1) and the chi-square percentiles applied to the distribution of Q to provide, respectively, point and interval estimation of τ.
Although numerous studies on estimation of genetic parameters of apple fruit phenotypes have been conducted (Durel et al. 1998; Alspach and Oraguzie 2002; Kouassi et al. 2009; Kumar et al. 2011), there has not yet been a systematic dissection of genetic variance of apple phenotypes into additive and nonadditive variances.
However, this method is time consuming since it involves extensive computations, including geometric direction estimation and numerous iterations of wavelet transform.
Further, being based on a parameterized network evolution model, it requires the estimation of numerous model parameters.
However, model specification and selection with this approach is cumbersome and requires the estimation of numerous parameters.
