Introduction.- Data management and software.- Advice for teachers.- Exploration.- Linear regression.- Generalised linear modelling.- Additive and generalised additive modelling.- Introduction to mixed modelling.- Univariate tree models.- Measures of association.- Ordination--first encounter.- Principal component analysis and redundancy analysis.- Correspondence analysis and canonical correspondence analysis.- Introduction to discriminant analysis.- Principal coordinate analysis and non-metric multidimensional scaling.- Time series analysis--Introduction.- Common trends and sudden changes.- Analysis and modelling lattice data.- Spatially continuous data analysis and modelling.- Univariate methods to analyse abundance of decapod larvae.- Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugual.- Crop pollination by honeybees in an Argentinean pampas system using additive mixed modelling.- Investigating the effects of rice farming on aquatic birds with mixed modelling.- Classification trees and radar detection of birds for North Sea wind farms.- Fish stock identification through neural network analysis of parasite fauna.- Monitoring for change: using generalised least squares, nonmetric multidimensional scaling, and the Mantel test on western Montana grasslands.- Univariate and multivariate analysis applied on a Dutch sandy beach community.- Multivariate analyses of South-American zoobenthic species--spoilt for choice.- Principal component analysis applied to harbour porpoise fatty acid data.- Multivariate analysis of morphometric turtle data--size and shape.- Redundancy analysis and additive modelling applied on savanna tree data.- Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico.- Estimating common trends in Portuguese fisheries landings.- Common trends in demersal communities on the Newfoundland-Labrador Shelf.- Sea level change and salt marshes in the Wadden Sea: a time series analysis.- Time series analysis of Hawaiian waterbirds.- Spatial modelling of forest community features in the Volzhsko-Kamsky reserve.

Analysing Ecological Data

The growing cost of tuning and managing computer systems is leading to out-sourcing of commercial services to hosting centers. These centers provision thousands of dense servers within a relatively small real-estate in order to host the applications/services of different customers who may have been assured by a service-level agreement (SLA). Power consumption of these servers is becoming a serious concern in the design and operation of the hosting centers. The effects of high power consumption manifest not only in the costs spent in designing effective cooling systems to ward off the generated heat, but in the cost of electricity consumption itself. It is crucial to deploy power management strategies in these hosting centers to lower these costs towards enhancing profitability. At the same time, techniques for power management that include shutting down these servers and/or modulating their operational speed, can impact the ability of the hosting center to meet SLAs. In addition, repeated on-off cycles can increase the wear-and-tear of server components, incurring costs for their procurement and replacement. This paper presents a formalism to this problem, and proposes three new online solution strategies based on steady state queuing analysis, feedback control theory, and a hybrid mechanism borrowing ideas from these two. Using real web server traces, we show that these solutions are more adaptive to workload behavior when performing server provisioning and speed control than earlier heuristics towards minimizing operational costs while meeting the SLAs.

http://www.cse.psu.edu/~axs53/csl/papers/metrics05a.pdf

Managing server energy and operational costs in hosting centers

The incidence of malaria in the East African highlands has increased since the end of the 1970s. The role of climate change in the exacerbation of the disease has been controversial, and the specific influence of rising temperature (warming) has been highly debated following a previous study reporting no evidence to support a trend in temperature. We revisit this result using the same temperature data, now updated to the present from 1950 to 2002 for four high-altitude sites in East Africa where malaria has become a serious public health problem. With both nonparametric and parametric statistical analyses, we find evidence for a significant warming trend at all sites. To assess the biological significance of this trend, we drive a dynamical model for the population dynamics of the mosquito vector with the temperature time series and the corresponding detrended versions. This approach suggests that the observed temperature changes would be significantly amplified by the mosquito population dynamics with a difference in the biological response at least 1 order of magnitude larger than that in the environmental variable. Our results emphasize the importance of considering not just the statistical significance of climate trends but also their biological implications with dynamical models.

https://www.pnas.org/content/pnas/103/15/5829.full.pdf

Malaria resurgence in the East African highlands: Temperature trends revisited

For stationary time series, the cross-covariance and the cross-correlation as functions of time lag $n$ serve to quantify the similarity of two time series. The latter measure is also used to assess whether the cross-correlations are statistically significant. For nonstationary time series, the analogous measures are detrended cross-correlations analysis (DCCA) and the recently proposed detrended cross-correlation coefficient, ${\ensuremath{\rho}}_{\mathrm{DCCA}}(T,n)$, where $T$ is the total length of the time series and $n$ the window size. For ${\ensuremath{\rho}}_{\mathrm{DCCA}}(T,n)$, we numerically calculated the Cauchy inequality $\ensuremath{-}1\ensuremath{\le}{\ensuremath{\rho}}_{\mathrm{DCCA}}(T,n)\ensuremath{\le}1$. Here we derive $\ensuremath{-}1\ensuremath{\le}{\ensuremath{\rho}}_{\mathrm{DCCA}}(T,n)\ensuremath{\le}1$ for a standard variance-covariance approach and for a detrending approach. For overlapping windows, we find the range of ${\ensuremath{\rho}}_{\mathrm{DCCA}}$ within which the cross-correlations become statistically significant. For overlapping windows we numerically determine---and for nonoverlapping windows we derive---that the standard deviation of ${\ensuremath{\rho}}_{\mathrm{DCCA}}(T,n)$ tends with increasing $T$ to $1/T$. Using ${\ensuremath{\rho}}_{\mathrm{DCCA}}(T,n)$ we show that the Chinese financial market's tendency to follow the U.S. market is extremely weak. We also propose an additional statistical test that can be used to quantify the existence of cross-correlations between two power-law correlated time series.

/pdf/statistical-tests-for-power-law-cross-correlated-processes-3oo0qn3yjc.pdf

Statistical tests for power-law cross-correlated processes

/pdf/a-bayesian-tutorial-for-data-assimilation-3s0kyk5wma.pdf

A Bayesian tutorial for data assimilation

Time series analysis and its applications

Consumers often buy higher ranked brand after they are bored using current brand goods This may be analyzed utilizing matrix Suppose past purchasing data are set input and current purchasing data are set output, then transition matrix is identified using past and current data If all brand selections are composed by the upper shifts, then the transition matrix becomes an upper triangular matrix The goods of the same brand group would compose the Block Matrix in the transition matrix Condensing the variables of the same brand group into one, analysis becomes easier to handle and the transition of Brand Selection can be easily grasped We have made a questionnaire investigation concerning automobile purchase before In that paper, the questionnaire was carried out mainly on an urban area In this paper, we make an investigation on a rural area The questionnaire investigation to automobile purchasing case is conducted and above structure is confirmed We can make forecast utilizing the estimated transition matrix We can foresee the future purchase and the producers can make effective

/pdf/analysis-of-the-matrix-structure-in-the-preference-shift-of-38b6wy20lt.pdf

Analysis of the Matrix Structure in the Preference Shift of Customer Brand Selection for Automobile

How to allocate goods in shop shelves makes great influence to sales amount. Searching best fit allocation of goods to shelves is a kind of combinatorial problem. This becomes a problem of integer programming and utilizing genetic algorithm may be an effective method. Reviewing past researches, there are few researches made on this. In this paper, optimization in allocating goods to shop shelves is investigated. Problem description is executed. Utilizing genetic algorithm, optimum solution is pursued and verified by a simple numerical example. Various patterns of problems must be examined hereafter.

Optimization in Allocating Goods to Shop Shelves

PROBLEM TO BE SOLVED: To provide a state monitoring method for a periodical moving body by steepness calculation in a half-triangular derivative shock wave model, a monitoring device therefor, a monitoring system therefor, a computer program and a recording medium, in order to determine an abnormality by a triangular shock wave coming from a vibration signal including derivative shock waves before and after the shock wave, concerning abnormality generation of a rotator. SOLUTION: The state monitoring method, the state monitoring system, the state monitoring device, the computer program and the recording medium are constructed, by which the vibration signal of the periodical moving body is measured, and a kurtosis simplified calculation method when the shock wave is generated and when the derivative shock wave is generated only on one side of the time progression direction is derived relative to measured data such as the number of intervals of the shock waves, and a facility abnormality or the like is determined accurately by comparing the result with a numerical value at the normal time. COPYRIGHT: (C)2006,JPO&NCIPI

友紀樋口

Papers

Time series analysis and its applications

Analysis of the Matrix Structure in the Preference Shift of Customer Brand Selection for Automobile

Optimization in Allocating Goods to Shop Shelves

State monitoring method for periodical moving body, monitoring device, monitoring system, computer program and recording medium

Identification of Brand Transition Matrix in the Case of New Brand Entry

友紀 樋口

Papers

Time series analysis and its applications

Analysis of the Matrix Structure in the Preference Shift of Customer Brand Selection for Automobile

Optimization in Allocating Goods to Shop Shelves

State monitoring method for periodical moving body, monitoring device, monitoring system, computer program and recording medium

Identification of Brand Transition Matrix in the Case of New Brand Entry

友紀樋口