The Estimation of Distance Functions
Estimation of Input Distance Functions: A System Approach
... and estimation 1 of production functions using data on inputs and outputs is among the oldest empirical problems in economics dating back at least as early as the 19th century (Chambers, ...processes, ...
21
Econometric Estimation of Distance Functions and Associated Measures of Productivity and Efficiency Change
... One of the advantages of the Bayesian approach is that it is also straightforward to draw valid finite-sample inferences about the unknown parameters in ways that are often more inform[r] ...
26
A Bayesian Approach To Imposing Curvature On Distance Functions
... In our empirical application to 17 European railways, our estimates of (relative) technical efficiency seem more sensitive to the random versus fixed effects assumptions than to the imposition of regularity constraints. ...
27
ON THE ESTIMATION OF DISTANCE DISTRIBUTION FUNCTIONS FOR POINT PROCESSES AND RANDOM SETS
... classical estimation procedure, p and λ is estimated from the whole window of observation, while the numerator is obtained only from a subwindow or by some form of edge ...the estimation of second-order ...
5
Estimation bias under model selection for distance sampling detection functions
... The second factor affecting the bias is the use of the monotonicity constraint in conjunction with the number of parameters of the selected models. The monotonicity constraint is used because we expect a priori that the ...
16
Empirical characteristic functions-based estimation and distance correlation for locally stationary processes
... Fitting stable distributions to data requires suitable parameter estimators. Due to the lack of closed form representation of the density and the cdf for many parameter values, the application of classical maximum ...
55
On parameter estimation with the Wasserstein distance
... Wasserstein distance between model distributions and the empirical distribution of the ...Wasserstein distance estimators, complementing results derived by Bassetti, Bodini and Regazzini in ...likelihood ...
24
Approximating the Distance to Monotonicity of Boolean Functions
... √ n log n, it outputs close with probability at least 2/3. 2. If ε f ≥ ε, it outputs far with probability at least 2/3. The algorithm ApproxMono is described in Algorithm 1. In the algorithm and its analy- sis, we use ...
30
Mixture models for distance sampling detection functions
... detection functions can be used ...detection functions appear useful when dealing with line transect data that has a spike in detection probability at small dis- tances, though we note that it is better to ...
19
Distance sampling detection functions : 2D or not 2D?
... Conventional distance sampling (CDS) methods assume that animals are uniformly distributed in the vicinity of lines or ...formulating distance sampling models as survival models, we show that using time to ...
30
Change Point Estimation of Bilevel Functions
... techniques suggested in Joseph and Pavlidis (1993), Joseph and Pavlidis (1994), and Esedoglu (2004). Proposition 1 of Joseph and Pavlidis (1993) suggested approximating σ by the distance from the last local maxima ...
10
Directional functions for orientation distribution estimation
... Apart from Tuch [37], numerous methods have been proposed. Specifically, Jansons et. al. [15] solves the problem in the Fourier domain using amaximum entropy parametrization, although their numerical approximations are ...
41
Resolution of the Wicksell's equation by Minimum Distance Estimation
... The proposed algorithm has been applied on a given material (uranium dioxide, imaged by EBSD). It has been shown that the normal distribution has led to the best results, among all investigated distribution ...
14
Minimum mean square distance estimation of a subspace
... an arbitrary matrix, and , are hy- pergeometric functions of matrix arguments, see e.g., [27] for their definitions. The Bingham and the von Mises Fisher distri- butions have been proposed in various applications, ...
13
ON THE COMPUTATION OF EDIT DISTANCE FUNCTIONS
... is indicated. The rest of the paper is organized as follows: Section 2 gives some of the general definitions for the edit distance function, such as colored regularity graphs. Section 3 defines and categorizes ...
20
On the Commensurability of Directional Distance Functions
... Shephard’s distance functions are widely used instruments for characterizing technology and for estimating efficiency in contemporary economic theory and ...directional distance function, which in ...
17
Distance Transforms of Sampled Functions
... classical distance transforms of binary images, where the binary image is replaced by an arbitrary function on a ...two functions, which is an important operation in grayscale ...Euclidean distance ...
14
Distance estimation by foraging honeybees
... An observation hive was placed half way up a steep mountain slope (inclination 15–37 ˚) (Heran and Wanke, 1952). Foragers collected sugar water uphill and downhill from the hive. Only two out of seven experiments gave ...
8
Bayesian Estimation of Genomic Distance
... Subtracting the number of chromosomes in the ge- The aim of the Bayesian approach is to generate the nome for which the distances are known from S gives probability distribution of inversions and translocations an ...
10
Virtual Distance Estimation in a CAVE
... virtual distance estimation research has been conducted using an HMD, so those results may provide a good starting point, but they fail to predict the interplay between virtual and physical distance ...
18