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ISM Symposium on Environmental Statistics 2015 (First Report)

admin_risk — Tue, 11 Nov 2014 04:53:04 +0000

ISM Symposium on Environmental Statistics 2015
(First Report)

Data & Time

Feb. 24 (Wed), 2015 10:00-17:30

Venue

The Institute of Statistical Mathematics, 2rd Floor Auditorium

Topics

In order to enhance the understanding of the global environment, statistical science is extremely important. Centered around the topic of directional statistics, we are holding a symposium in order to better develop research on statistical theory which can be applied to solve specific issues in the fields of environmental and ecological data.

Joint Hosting

The Graduate University for Advanced Studies [Sokendai]
NPO Japan Institute of Environmental Statistics

Organizers

Koji Kanefuji (ISM)
Alan Welsh (ANU)
Atsushi Yoshimoto (ISM)
Kunio Shimizu (ISM)
Kenichiro Shimatani (ISM)

Invited Speakers

Pierre R. L. Dutilleul (McGill University,Canada)
Louis-Paul Rivest (Université Laval, Canada)
Alan Welsh (The Australian National University, Australia

Symposia which were held in past

ISM Symposium on Environmental Statistics 2014
ISM Symposium on Environmental Statistics 2013

Training Seminar on Applied Statistical Analysis with R for Forestry Studies-Series II 2014

admin_risk — Thu, 11 Sep 2014 00:45:09 +0000

“Training Seminar on Applied Statistical Analysis with R for Forestry Studies-Series II 2014” be held in Tribhuvan University, Institute of Forestry, Pokhara, Nepal on September 19-21.

Training Seminar on Applied Statistical Analysis
with R for Forestry Studies—Series II 2014

Organizers

Risk Analysis Research Center, Institute of Statistical Mathematics

Co- Organizers

Tribhuvan University, Institute of Forestry, Pokhara, Nepal

Date

September 19 (Fri) – 21 (Sun), 2014

Venue

Mount Kailash Resort Ltd.
Lake Side-6, Pokhara, Nepal

ISM Symposium on Environmental Statistics 2014 (Update:Jan 20, 2014)

admin_risk — Fri, 27 Dec 2013 05:45:07 +0000

ISM Symposium on Environmental Statistics 2014
(Second Report)

Date & Time

Feb. 5 (Wed), 2014 10:00～17:30

Venue

2rd Floor Auditorium
The Institute of Statistical Mathematics,
10-3 Midori-cho Tachikawa, Tokyo 190-8562, Japan

Topics

Joint Hosting

Ministry of Education, Culture, Sports, Science and Technology (MEXT)

Organizers

Koji Kanefuji (ISM)
Alan Welsh (ANU)
Atsushi Yoshimoto (ISM)
Kunio Shimizu (Keio University)
Kenichiro Shimatani (ISM)

Program(Tentative)

10:30-10:40
Opening Address

10:40-11:20
The prediction of random effects in hurdle models
E. Cantoni*1, J. Mills Flemming*2 and A.H. Welsh*3
(*1:The University of Geneva, *2:Dalhousie University, *3:The Australian National University)

11:20-12:00
Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size
Anne Chao (Institute of Statistics, National Tsing Hua University)

—–Lunch—–

13:00-13:40
Semivarying coefficient models for capture-recapture data
:Colony size estimation for the little penguin
Richard Huggins (Department of Mathematics and Statistics, The University of Melbourne)

13:40-14:20
Estimation of Markov transition probabilities for ecological communities using dynamic site occupancy models
Keiichi Fukaya (The Institute of Statistical Mathematics)

14:20-15:00
Investigating species interactions in a fish community
Hideyasu Shimadzu, Maria Dornelas, Peter A. Henderson, Anne E. Magurran(School of Biology, University of St Andrews)

—–Coffee Break—–

15:10-15:50
A new probability model for cylindrical data
Kunio Shimizu(Department of Mathematics, Keio University)

15:50-16:30
Influence diagnostics in regression and time series models of circular data.
Shuangzhe Liu(Department of Mathematics and Statistics, University of Canberra)

16:30-16:35
Closing address

Abstract

[1] The prediction of random effects in hurdle models
E. Cantoni1, J. Mills Flemming2 and A.H. Welsh3 1:The University of Geneva, 2:Dalhousie University, 3:The Australian National University
For many endangered marine species, such as sharks, the only available data on their abundance are counts of when they are caught unintentionally in a fishery (that is, as bycatch). These data typically involve a larger number of zero counts (indicating that none were caught as bycatch in a particular haul, for example) and very few positive counts (obtained if one or more are caught as bycatch in a haul) and are clustered because hauls are clustered within trips which may also be clustered within vessels. We are therefore interested in fitting models to the data which accommodate the zeros and the clustering, and then using these models to properly answer important scientific questions such as predicting the probability of bycatch (and other cluster specific targets). We will discuss the prediction of random effects and functions of random effects in the context of fitting hurdle models (also referred to as two-part, zero-altered or separated models) with random effects for modelling clustered count data with excess zeros. We implement empirical best predictors of the random effects and other cluster specific targets. We discuss estimating their prediction mean squared errors using a fast bootstrap approach. The methodology is validated through simulation and demonstrated using real data on critically endangered hammerhead sharks where the prediction of cluster specific targets is essential for informing conservation and management decisions.

[1] The prediction of random effects in hurdle models

E. Cantoni*1, J. Mills Flemming*2 and A.H. Welsh*3
*1:The University of Geneva, *2:Dalhousie University, *3:The Australian National University

For many endangered marine species, such as sharks, the only available data on their abundance are counts of when they are caught unintentionally in a fishery (that is, as bycatch). These data typically involve a larger number of zero counts (indicating that none were caught as bycatch in a particular haul, for example) and very few positive counts (obtained if one or more are caught as bycatch in a haul) and are clustered because hauls are clustered within trips which may also be clustered within vessels. We are therefore interested in fitting models to the data which accommodate the zeros and the clustering, and then using these models to properly answer important scientific questions such as predicting the probability of bycatch (and other cluster specific targets).
We will discuss the prediction of random effects and functions of random effects in the context of fitting hurdle models (also referred to as two-part, zero-altered or separated models) with random effects for modelling clustered count data with excess zeros. We implement empirical best predictors of the random effects and other cluster specific targets. We discuss estimating their prediction mean squared errors using a fast bootstrap approach. The methodology is validated through simulation and demonstrated using real data on critically endangered hammerhead sharks where the prediction of cluster specific targets is essential for informing conservation and management decisions.

[2] Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size
ANNE CHAO (Institute of Statistics, National Tsing Hua University)
In this talk, I will review a recent proposal of an integrated sampling, rarefaction, and extrapolation methodology to compare species richness of a set of communities based on samples of equal completeness (as measured by sample coverage) instead of equal size. The concept of “sample coverage” (or simply “coverage”) was originally developed by the founder of modern computer science, Alan Turing, and I. J. Good. It is a measure of sample completeness, giving (in our context) the proportion of the total number of individuals in a community that belong to the species represented in the sample. Traditional rarefaction or extrapolation to equal-sized samples can misrepresent the relationships between the richnesses of the communities being compared, because a sample of a given size may be sufficient to fully characterize the lower-diversity community but insufficient to characterize the richer community. Thus the traditional method systematically biases the degree of differences between community richnesses. A new analytic method for seamless coverage-based rarefaction and extrapolation was recently developed by Chao and Jost (2012). I will use examples to show that this method yields less-biased comparisons of richness between communities, and manages this with less total sampling effort. Several hypothetical and real examples demonstrate these advantages. I will also briefly mention the extension of this new rarefaction/extrapolation method to other measures of biodiversity, including Shannon diversity (the exponential of entropy) and Simpson diversity (the inverse Simpson concentration). (This is a joint work with Lou Jost)

[2] Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size

ANNE CHAO
(Institute of Statistics, National Tsing Hua University)

In this talk, I will review a recent proposal of an integrated sampling, rarefaction, and extrapolation methodology to compare species richness of a set of communities based on samples of equal completeness (as measured by sample coverage) instead of equal size. The concept of “sample coverage” (or simply “coverage”) was originally developed by the founder of modern computer science, Alan Turing, and I. J. Good. It is a measure of sample completeness, giving (in our context) the proportion of the total number of individuals in a community that belong to the species represented in the sample. Traditional rarefaction or extrapolation to equal-sized samples can misrepresent the relationships between the richnesses of the communities being compared, because a sample of a given size may be sufficient to fully characterize the lower-diversity community but insufficient to characterize the richer community. Thus the traditional method systematically biases the degree of differences between community richnesses. A new analytic method for seamless coverage-based rarefaction and extrapolation was recently developed by Chao and Jost (2012). I will use examples to show that this method yields less-biased comparisons of richness between communities, and manages this with less total sampling effort. Several hypothetical and real examples demonstrate these advantages. I will also briefly mention the extension of this new rarefaction/extrapolation method to other measures of biodiversity, including Shannon diversity (the exponential of entropy) and Simpson diversity (the inverse Simpson concentration).
(This is a joint work with Lou Jost)

[3] Semivarying coefficient models for capture-recapture data: Colony size estimation for the little penguin
Richard Huggins (Department of Mathematics and Statistics, The University of Melbourne)
To accommodate seasonal effects that change from year to year into models for the size of an open population we consider a time-varying coefficient model. We fit this model to a capture-recapture data set collected on the little penguin in south-eastern Australia over a 25 year period, using Jolly-Seber type estimators and nonparametric P-spline techniques. The time-varying coefficient model identified strong changes in the seasonal pattern across the years which we further examine using functional data analysis techniques. (Joint work with Jakub Stoklosa of The University of New South Wales and Peter Dann from the Phillip Island Nature Parks)

[3] Semivarying coefficient models for capture-recapture data: Colony size estimation for the little penguin

Richard Huggins
(Department of Mathematics and Statistics, The University of Melbourne)

To accommodate seasonal effects that change from year to year into models for the size of an open population we consider a time-varying coefficient model. We fit this model to a capture-recapture data set collected on the little penguin in south-eastern Australia over a 25 year period, using Jolly-Seber type estimators and nonparametric P-spline techniques. The time-varying coefficient model identified strong changes in the seasonal pattern across the years which we further examine using functional data analysis techniques.
(Joint work with Jakub Stoklosa of The University of New South Wales and
Peter Dann from the Phillip Island Nature Parks)

[4] Estimation of Markov transition probabilities for ecological communities using dynamic site occupancy models
Keiichi Fukaya (The Institute of Statistical Mathematics)
Dynamics of ecological communities, especially of sessile organisms, have often been described by Markov models where changes in relative abundance of species are summarized by transition probability matrices. Transition probabilities can be estimated from time series data of species occupancy states collected at fixed sampling points, although a naive estimation of transition probabilities may not be robust when resampling error exists. Here I present a model based approach for the estimation of transition probabilities of the communities in which transition probabilities are estimated in the framework of multistate dynamic site occupancy model. The model explicitly describes both transitions among occupancy states and observation processes and hence takes the effect of resampling error in the estimation of transition probabilities into account. I also show that the resampling error rate can be estimated without any additional data other than the annual (or seasonal) data of occupancy states, suggesting the usefulness of this framework to estimate transition probabilities with limited field data.

[4] Estimation of Markov transition probabilities for ecological communities using dynamic site occupancy models

Keiichi Fukaya
(The Institute of Statistical Mathematics)

Dynamics of ecological communities, especially of sessile organisms, have often been described by Markov models where changes in relative abundance of species are summarized by transition probability matrices. Transition probabilities can be estimated from time series data of species occupancy states collected at fixed sampling points, although a naive estimation of transition probabilities may not be robust when resampling error exists. Here I present a model based approach for the estimation of transition probabilities of the communities in which transition probabilities are estimated in the framework of multistate dynamic site occupancy model. The model explicitly describes both transitions among occupancy states and observation processes and hence takes the effect of resampling error in the estimation of transition probabilities into account. I also show that the resampling error rate can be estimated without any additional data other than the annual (or seasonal) data of occupancy states, suggesting the usefulness of this framework to estimate transition probabilities with limited field data.

[5] Investigating species interactions in a fish community
Hideyasu Shimadzu, Maria Dornelas, Peter A. Henderson, Anne E. Magurran (School of Biology, University of St Andrews)
Understanding the interactions between species within an ecological community is a key challenge in biodiversity research. Here, we focus on monthly time series records of an exceptionally well-documented estuarine fish assemblage in the Bristol Channel. Given the multi-species time series data, we have developed a model for a multivariate feedback system in which the outputs can be the inputs and vice versa. The model assumes linear interactions between species as a tractable approximation. To examine the extent to which the abundance of a given species is driven by the other species, we have analysed the model in the spectrum domain and calculated the contribution ratio of each species at each frequency. The result suggests that our modelling approach dealing with an ecological community as a multivariate feedback system provides new insights into species interactions. We demonstrate how it enables further analysis into ecologically relevant groups of species that underpin the functioning of the system as a whole.

[5] Investigating species interactions in a fish community

Hideyasu Shimadzu, Maria Dornelas, Peter A. Henderson, Anne E. Magurran
(School of Biology, University of St Andrews)

Understanding the interactions between species within an ecological community is a key challenge in biodiversity research. Here, we focus on monthly time series records of an exceptionally well-documented estuarine fish assemblage in the Bristol Channel. Given the multi-species time series data, we have developed a model for a multivariate feedback system in which the outputs can be the inputs and vice versa. The model assumes linear interactions between species as a tractable approximation. To examine the extent to which the abundance of a given species is driven by the other species, we have analysed the model in the spectrum domain and calculated the contribution ratio of each species at each frequency. The result suggests that our modelling approach dealing with an ecological community as a multivariate feedback system provides new insights into species interactions. We demonstrate how it enables further analysis into ecologically relevant groups of species that underpin the functioning of the system as a whole.

[6] A new probability model for cylindrical data
Kunio Shimizu (Department of Mathematics, Keio University)
Environmental data such as wind speed and direction, ozone concentration and wind direction, and earthquakes magnitudes and successive turning angles of the epicenter are considered data on the cylinder, i.e. the dada consist of a combination of linear and circular observations. In the literature, probability distributions are introduced by Mardia and Sutton (1978, JRSS), Johnson and Wehrly (1978, JASA) and Kato and Shimizu (2008, JSPI) as models for cylindrical data. In this talk, we give a new probability distribution which is derived as a conditional distribution of a mixture of trivariate normal distributions. The distribution should be called the Pearson Type VII distribution on the cylinder, and includes the Mardia-Sutton and Kato-Shimizu cylindrical distributions as special cases. We study some distributional properties such as marginal and conditional distributions, modality, circular-linear correlation, regression, and so on. (This is joint work with Shonosuke Sugasawa, The University of Tokyo, and Shogo Kato, The Institute of Statistical Mathematics.)

[6] A new probability model for cylindrical data

Kunio Shimizu
(Department of Mathematics, Keio University)

Environmental data such as wind speed and direction, ozone concentration and wind direction, and earthquakes magnitudes and successive turning angles of the epicenter are considered data on the cylinder, i.e. the dada consist of a combination of linear and circular observations. In the literature, probability distributions are introduced by Mardia and Sutton (1978, JRSS), Johnson and Wehrly (1978, JASA) and Kato and Shimizu (2008, JSPI) as models for cylindrical data. In this talk, we give a new probability distribution which is derived as a conditional distribution of a mixture of trivariate normal distributions. The distribution should be called the Pearson Type VII distribution on the cylinder, and includes the Mardia-Sutton and Kato-Shimizu cylindrical distributions as special cases. We study some distributional properties such as marginal and conditional distributions, modality, circular-linear correlation, regression, and so on.
(This is joint work with Shonosuke Sugasawa, The University of Tokyo, and Shogo Kato, The Institute of Statistical Mathematics.)

[7] Influence diagnostics in regression and time series models of circular data.
Shuangzhe Liu (Department of Mathematics and Statistics, University of Canberra)
Distributional studies, regression and time series models have played important roles in statistical analysis of circular data. In this paper, we consider a likelihood approach to identify possible influential observations in circular data for these models. We use the maximum likelihood estimation and influence diagnostics methods. The observed information matrices and normal curvatures are derived. Simulated and real data examples are then provided to illustrate our approach and results to be useful.

[7] Influence diagnostics in regression and time series models of circular data.

Shuangzhe Liu
(Department of Mathematics and Statistics, University of Canberra)

Distributional studies, regression and time series models have played important roles in statistical analysis of circular data. In this paper, we consider a likelihood approach to identify possible influential observations in circular data for these models. We use the maximum likelihood estimation and influence diagnostics methods. The observed information matrices and normal curvatures are derived. Simulated and real data examples are then provided to illustrate our approach and results to be useful.

Joint International Symposium By Japan and Czech RepublicData Acquisition, Statistical Modeling and Decision-Making Toward Better Forestry

admin_risk — Mon, 30 Sep 2013 04:45:58 +0000

“Joint International Symposium By Japan and Czech RepublicData Acquisition, Statistical Modeling and Decision-Making Toward Better Forestry” be held in Czech University of Life Sciences Prague on October 8-9.

Training on Introduction to Statistical Analysis in “R” for Forest Resource Management

admin_risk — Tue, 24 Sep 2013 15:00:50 +0000

“Training on Introduction to Statistical Analysis in “R” for Forest Resource Management” be held in Forest and Wildlife Training Center, Hanoi Street, Phnom Penh Thmey, Cambodia, on September 25-27.

Seminar Announcement

admin_risk — Mon, 19 Aug 2013 05:16:39 +0000

Risk Analysis Research Center holds the seminar on September 6, 2013.

Date

Friday 6th September 2013, 4pm-5:30pm

Place

Seminar Room 5 (3F, D313),
The Institute of Statistical Mathematics,
10-3 Midori-cho Tachikawa, Tokyo 190-8562, Japan
Access

Speaker

Marie Huskova (Charles University, Czech Republic)

Title

Change Point Analysis: Robust and Rank Based Procedures and Applications

Abstract

Change point analysis concerns procedures on stability of statistical models. The basic scheme can be formulated as follows: a sequence of observations $Y_1,\dots,Y_n$ obtained at the ordered time points $t_1<\dots There are numerous applications in meteorology, climatology, hydrology or environmental studies, econometric time series, statistical quality control among others.
After an introduction the talk will focus on robust and rank based procedures for detection of changes. Their description will be accompanied by theoretical properties and simulation results.
The last part of the talk will concern robust sequential procedures for detection of changes in Capital Assets Pricing Model (CAPM). Theoretical results together with an application to real data set will be presented.

ISM Symposium on Environmental Statistics 2013 (Last Updated; Jan 10, 2013)

admin_risk — Thu, 27 Dec 2012 06:47:08 +0000

ISM Symposium on Environmental Statistics 2013

Date & Time

Jan. 25 (Fri), 2013 13:00

Venue

3rd Floor Seminar Room 1,2,
The Institute of Statistical Mathematics,
10-3 Midori-cho Tachikawa, Tokyo 190-8562, Japan

Program (Tentative)

13:00-13:05	Opening Address: Junji Nakano (ISM)
13:05-13:50	[1] Alan Welsh (The Australian National University)	Is it there? Analysing occupancy surveys (Abstract)
13:50-14:35	[2] Janice Scealy (The Australian National University)	Fitting Kent models to compositional data with small concentration (Abstract)
14:35-15:05	[3] Toshihiro Abe (Tokyo University of Science)	Circular and axial distributions with an application for fallen tree data (Abstract)
15:05-15:20	Coffee Break
15:20-16:05	[4] Shuangzhe Liu (University of Canberra), Min-zhen Wang (Keio University), K. Shimizu (Keio University) , and A. SenGupta (Indian Statistical Institute)	Influence diagnostics in asymmetric circular-linear multivariate regression models (Abstract)
16:05-16:35	[5] Kenichiro Shimatani (ISM)	Circular statistics for animal movement ecology and movement ecology for circular statistics (Abstract)
16:35-17:05	[6] Min-zhen Wang and Kunio Shimizu (Keio University)	Cylindrical Distributions with Application to Environmental Data (Abstract)
17:05-17:10	Closing address: Tomoyuki Higuchi (ISM: Director-General)

Organizing Committee

Koji Kanefuji (ISM)
Kunio Shimizu (Keio University, ISM)
Atsushi Yoshimoto (ISM)
Kenichiro Shimatani (ISM)

Abstract

[1] Alan Welsh
Is it there? Analysing occupancy surveys

We will discuss some general methods which deal with nondetection and then consider a currently popular method called occupancy modelling in more detail. We will show how we use statistical thinking to understand occupancy modelling and evaluate its properties. It is obviously important to understand when a method will or will not work well and to understand its limitations. We will see that occupancy models are more difficult to fit and interpret than is generally appreciated because the estimating equations often have multiple solutions and the estimates are unstable when the data are sparse. When the abundance of a species varies from site to site the standard analysis runs into difficulties and in this case, occupancy modelling can be just as poor as analyses which ignore nondetection completely. This raises broader philosophical questions about the use of incorrect models and the value of trying to make complicated adjustments in difficult problems.

[2]Janice Scealy
Fitting Kent models to compositional data with small concentration

Compositional data can be transformed to directional data by the square root transformation and then modelled by using the Kent distribution. The current approach for estimating the parameters in the Kent model for compositional data relies on a large concentration assumption which assumes that the majority of the transformed data is not distributed too close to the boundaries of the positive orthant. When the data is distributed close to the boundaries with large variance significant folding may result. To treat this case we propose new estimators of the parameters derived based on the actual folded Kent distribution which are obtained via the EM algorithm. We show that these new estimators significantly reduce the bias in the current estimators when both the sample size and amount of folding is moderately large. We also propose using a saddlepoint density approximation for the Kent distribution normalising constant in order to more accurately estimate the shape paramet ers when the concentration is small or only moderately large.

[3] Toshihiro Abe
Circular and axial distributions with an application for fallen tree data

We present a retrospective method for studying forest disturbance regimes, and especially the role of windthrows, based on circular and axial statistical models of directions of fallen logs. This approach was applied to fallen log data from three areas of pristine Picea abies-dominated boreal forests in northern Europe. The data consisted of 5 plots from each of the three areas, totaling 15 plots and covering an area of 24 ha.The disturbance history of the plots, which varied from area to area, was known from previous detailed studies. Our results suggested the utility of circular and axial distributions of fallen logs and their statistical models for retrospective assessments of forest disturbance regimes.

[4] Shuangzhe Li，Min-zhen Wang, K. Shimizu, and A. SenGupta
Influence diagnostics in asymmetric circular-linear multivariate regression models

Distributional studies and regression models have played important roles in statistical analysis of circular data. Asymmetric circular-linear multivariate regression models (SenGupta and Ugwuowo, 2006) are motivated by and applied to predict some environmental characteristics based on both circular and linear predictors. In this paper, we consider a likelihood approach (Cook, 1986) to study influence diagnostic analysis for these models, using the maximum likelihood estimation and influence diagnostics methods. The observed information matrices and normal curvatures are derived. Simulated and real data examples are then provided to illustrate our approach and establish the utility of our results.

[5] Kenichiro Shimatani
Circular statistics for animal movement ecology and movement ecology for circular statistics

Animal movement ecology will advance in parallel to developments in circular statistics, and the development of circular statistics will be promoted by the practical demands made from movement ecology. Here I will present an example of such studies. Movement trajectories of an animal are often more oriented or tortuous than expected from simple (correlated) random models. On the basis of the recently developed circular auto-regressive model, a new movement model was introduced and applied to GPS trajectories of a seabird. The proposed model enables us to evaluate the effects of external factors on movements separately from the animal’s internal state. For example, maximum likelihood estimates and model selection by AIC suggested that in one homing flight section, the seabird intended to fly toward the home island, but misjudged its navigation and was driven off-course by winds, while in the subsequent flight section, the seabird reset the focal direction, navigated the flight under strong wind conditions, and succeeded in approaching the nest.

[6] Min-zhen Wang and Kunio Shimizu
Cylindrical Distributions with Application to Environmental Data

A cylindrical distribution means the joint distribution of a linear random variable and a circular random variable such as wind speed and direction and the concentration of a pollutant and wind direction. A pair of realization may be identified with a point on the cylinder. Johnson and Wehrly (1978) and Mardia and Sutton (1978) give fundamental models on the cylinder, and Kato and Shimizu (2008) extend some of the Mardia-Sutton and Johnson-Wehrly models. In this talk we introduce some cylindrical distributions and fit them to the periwinkle dataset in Fisher (1993).

References
1. Fisher, N. I. (1993). Statistical Analysis of Circular Data, Cambridge University Press, Cambridge.
2. Johnson, R. A. and Wehrly, T. E. (1978). Some angular-linear distributions and related regression models, Journal of the American Statistical Association, 73, 602-606.
3. Kato, S. and Shimizu, K. (2008). Dependent models for observations which include angular ones, Journal of Statistical Planning and Inference, 138, 3538-3549.
4. Mardia, K. V. and Sutton, T. W. (1978). A model for cylindrical variables with applications, Journal of the Royal Statistical Society, B40, 229-233.

International Summer Seminar on Forest Resource Management

admin_risk — Mon, 20 Aug 2012 07:00:23 +0000

Japan Society of Forest Planning, Risk Analysis Research Center and Ecosystem Adaptability Global COE (Tohoku University) holds “International Summer Seminar on Forest Resource Management Consideration of Multifunctionality and Eco-Services Concerned“, Integrated Terrestrial Field Station (Kawatabi Field Center) Field Science Center, Graduate School of Agricultural Science, Tohoku University (Kawatabi, Miyagi) on August 29-31, 2012.

Joint International Symposium By Japan, Korea and Taiwan Sustainable Forest Ecosystem Management in Rapidly Changing World

admin_risk — Mon, 20 Aug 2012 05:43:45 +0000

“Joint International Symposium By Japan, Korea and Taiwan Sustainable Forest Ecosystem Management in Rapidly Changing World” be held in National Ilan University, Taiwan on September 12-14, 2012.

ISM SYMPOSIUM2012

admin_risk — Tue, 06 Mar 2012 04:09:08 +0000

“ISM SYMPOSIUM2012 Statistical Modeling and its Applications for Risk Analysis Joint International Symposium with Statistical Researchers from Prague, Czech” be held in The Institute of Statistical Mathematics (ISM) on March 24, 2012.

» Topics

ISM Symposium on Environmental Statistics 2015 (First Report)

Data & Time

Venue

Topics

Joint Hosting

Organizers

Invited Speakers

Symposia which were held in past

Training Seminar on Applied Statistical Analysis with R for Forestry Studies-Series II 2014

Organizers

Co- Organizers

Date

Venue

ISM Symposium on Environmental Statistics 2014 (Update:Jan 20, 2014)

Date & Time

Venue

Topics

Joint Hosting

Organizers

Program(Tentative)

Abstract

Joint International Symposium By Japan and Czech RepublicData Acquisition, Statistical Modeling and Decision-Making Toward Better Forestry

Training on Introduction to Statistical Analysis in “R” for Forest Resource Management

Seminar Announcement

Date

Place

Speaker

Title

Abstract

ISM Symposium on Environmental Statistics 2013 (Last Updated; Jan 10, 2013)

Program (Tentative)

Organizing Committee

Abstract

[1] Alan Welsh Is it there? Analysing occupancy surveys

[2]Janice Scealy Fitting Kent models to compositional data with small concentration

[3] Toshihiro Abe Circular and axial distributions with an application for fallen tree data

[4] Shuangzhe Li，Min-zhen Wang, K. Shimizu, and A. SenGupta Influence diagnostics in asymmetric circular-linear multivariate regression models

[5] Kenichiro Shimatani Circular statistics for animal movement ecology and movement ecology for circular statistics

[6] Min-zhen Wang and Kunio Shimizu Cylindrical Distributions with Application to Environmental Data

International Summer Seminar on Forest Resource Management

Joint International Symposium By Japan, Korea and Taiwan Sustainable Forest Ecosystem Management in Rapidly Changing World

ISM SYMPOSIUM2012

[1] Alan Welsh
Is it there? Analysing occupancy surveys

[2]Janice Scealy
Fitting Kent models to compositional data with small concentration

[3] Toshihiro Abe
Circular and axial distributions with an application for fallen tree data

[4] Shuangzhe Li，Min-zhen Wang, K. Shimizu, and A. SenGupta
Influence diagnostics in asymmetric circular-linear multivariate regression models

[5] Kenichiro Shimatani
Circular statistics for animal movement ecology and movement ecology for circular statistics

[6] Min-zhen Wang and Kunio Shimizu
Cylindrical Distributions with Application to Environmental Data