scholarly journals On a class of recursive estimators for spatially dependent observations

2021 ◽  
Vol 15 (2) ◽  
Author(s):  
Mohamed El Machkouri ◽  
Lucas Reding
Keyword(s):  
Dependent Observations ◽  
Recursive Estimators

scholarly journals Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask

2013 ◽  
Vol 6 (1) ◽  
pp. 1815-1858 ◽  
Author(s):  
S. P. Burton ◽  
R. A. Ferrare ◽  
M. A. Vaughan ◽  
A. H. Omar ◽  
R. R. Rogers ◽  
...  
Keyword(s):  
High Spectral Resolution ◽  
Dependent Observations ◽  
Aerosol Retrieval ◽  
Direct Measurements ◽  
Aerosol Classification ◽  
Lidar Ratio ◽  
Langley Research Center ◽  
High Spectral Resolution Lidar ◽  
Aerosol Types ◽  
Aerosol Type

Abstract. Aerosol classification products from the NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL-1) on the NASA B200 aircraft are compared with coincident V3.01 aerosol classification products from the CALIOP instrument on the CALIPSO satellite. For CALIOP, aerosol classification is a key input to the aerosol retrieval, and must be inferred using aerosol loading-dependent observations and location information. In contrast, HSRL-1 makes direct measurements of aerosol intensive properties, including the lidar ratio, that provide information on aerosol type. In this study, comparisons are made for 109 underflights of the CALIOP orbit track. We find that 62% of the CALIOP marine layers and 54% of the polluted continental layers agree with HSRL-1 classification results. In addition, 80% of the CALIOP desert dust layers are classified as either dust or dusty mix by HSRL-1. However, agreement is less for CALIOP smoke (13%) and polluted dust (35%) layers. Specific case studies are examined, giving insight into the performance of the CALIOP aerosol type algorithm. In particular, we find that the CALIOP polluted dust type is overused due to an attenuation-related depolarization bias. Furthermore, the polluted dust type frequently includes mixtures of dust plus marine aerosol. Finally, we find that CALIOP's identification of internal boundaries between different aerosol types in contact with each other frequently do not reflect the actual transitions between aerosol types accurately. Based on these findings, we give recommendations which may help to improve the CALIOP aerosol type algorithms.

scholarly journals Subsampling the mean of heavy-tailed dependent observations

2004 ◽  
Vol 25 (2) ◽  
pp. 217-234 ◽  
Author(s):  
Piotr Kokoszka ◽  
Michael Wolf
Keyword(s):  
Dependent Observations ◽  
The Mean ◽  
Heavy Tailed

Recursive estimators with Markovian jumps

2012 ◽  
Vol 61 (10) ◽  
pp. 1009-1016 ◽  
Author(s):  
Lirong Huang ◽  
Håkan Hjalmarsson
Keyword(s):  
Recursive Estimators

scholarly journals Filtering and predicting the cost of hidden perished items in an inventory model

2002 ◽  
Vol 15 (3) ◽  
pp. 235-245
Author(s):  
Lakhdar Aggoun ◽  
Lakdere Benkherouf
Keyword(s):  
Joint Distribution ◽  
Inventory Model ◽  
The State ◽  
Inventory Level ◽  
Discrete State ◽  
History Of ◽  
Accumulated Losses ◽  
The Cost ◽  
State Of The Environment ◽  
Recursive Estimators

This paper is concerned with a discrete time, discrete state inventory model for items of changing quality. Items are assumed to be in one of a finite number, M, of quality classes that are ordered in such a way that Class 1 contains the best quality and the last class contains the pre-perishable quality. The changes of items' quality are dependent on the state of the ambient environment. Furthermore, at each epoch time, items of different classes may be sold or moved to a lower quality class or stay in the same class. These items are priced according to their quality, and costs are incurred as items lose quality. Based on observing the history of the inventory level and prices, we propose recursive estimators as well as predictors for the joint distribution of the accumulated losses and the state of the environment.

Smoothing dependent observations

1994 ◽  
Vol 21 (3) ◽  
pp. 203-214 ◽  
Author(s):  
R. Fraiman ◽  
J. Meloche
Keyword(s):  
Dependent Observations
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