NMU Statisticians

Thursday, July 26, 2012

Job Openings 2

Wize Careers Consultants
Job | Urgent opening in forecasting and Adhoc mumbai location salary upto 12LPA

We are looking for Analyst/ Sr. analyst/ TL- Forecasting & Adhoc for one of the largest providers of business processing outsourcing services within the Banking and Financial Services sector based at Mumbai.

CTC : 4 L - 8 L , Band 3/4/5
Key Relationships with other functions/teams : Relationships with Analytics Practice Team, L&D, Quality

Qualifications :

Bachelor's degree (Master's or Ph. D. preferred) in a quantitative discipline: Mathematics, Economics, Operations Research, Statistics or a related field

Key Deliverables

Develop time series based models and identify trends and patterns based on macro economic indicators.

Perform adhoc analysis for Group risk and Product managers.

Banking secured and unsecured lending knowledge

Technical skills reqd

SQL/ SAS/VBA/MS OFFICE

Arima, econometrics

Experience

4 - 6 years + experience in portfolio management / analytics / financed

For TL position, Team Handling exp required.

Ability to work with multiple data sources and platforms

Experience in working in large MNC banks/financial institutions in Analytics, BI or technology

Knowledge of product life cycle of retail consumer/commercial banking products like cards, unsecured Installment Loans, mortgage, overdrafts

Skills in planning, organizing, project management, time management, and decision making Communication, networking and influencing skills

Administrative

Manage relationship with clients

Completes assigned tasks on time and with accuracy

Manage workload and handle escalations

Operations Reporting

Updates all trackers, Timesheet regularly and accurately

If the above profile suits you, pls forward your updated resume alongwith the below mentioned details :

Current location :
Ready to relocate to Mumbai :
Educational Qualification & Institute & Specialization :
Pls Specify if Graduation Part-time :
Total exp :
Organisation Type (BPO/Banking/IT ) etc :
Statistical techniques hands on : regressions, time series, cluster analysis, CHAID analysis, ARIMA, Earlang (specify) :
Specify knowledge of SAS , SQL, Macros , Excel, VBA :
Any exp of Working in large MNC banks/financial institutions IN Analytics (kindly specify) :
Exp of working in financial institutions - role and duration :
Products dealt with in Financial sphere :
Current Company :
Current Designation :
No. Of reportees :
Reporting to :
Last Salary Drawn (Monthly) Net & Gross :
CCTC (Fixed + variable) :
ECTC :
NP :
DoB :

Twinkle
twinkle@wizecareers.com
07838387625

Friday, July 20, 2012

Job Summary

Company: Sagar Wright Ltd
Location: Horsham, Southern Industries
Job Type: Full Time, Permanent
Years of Experience: 1+ years
Education Level: Bachelor's Degree
Career Level: Experienced (Non-Manager)
Salary: £25,000.00 - £40,000.00 per year+ Bonus + Pension + Flexi-Time

Pricing Analyst (Statistician, Actuarial Student or Graduate) –
Global General Insurance Company

About the Job
My client is looking for a Statistician, Actuarial Student or a graduate with strong knowledge of statistics and mathematics to join their Pricing Team. They are open to candidates from non-insurance backgrounds and have previously recruited people from the pharmaceutical industry, chemical companies and the banking sector. The key requirement is the ability to gather data and manipulate it for analytical and modelling purposes.

The Company
My client is one of the world's leading FTSE 100 general insurers with a proud heritage dating back 300 years. With net written premiums of GBP 6.7 billion (2009), they provide high quality, innovative insurance products and services in over 130 countries, setting new standards in the industry and employing over 23,000 people. They have achieved this through their strong track record of delivering award winning customer service and cutting edge propositions as well as our underwriting and claims expertise.

Why should this interest you?
Market Leading Training & Development - my clients Pricing Team offers you the opportunity to work closely with industry experts in the Actuarial and Statistical fields. Your training will encompass learning complex pricing techniques and gaining a clear understanding of the highly competitive and fast moving insurance market. High Profile Pricing Team - the Pricing Team is very well respected within the business at UK and Group level and has enjoyed significant investment over recent years. Working in the Pricing Team you can expect plenty of contact with senior individuals in the business. Internal Rotation - you will be given the chance to develop your skills in different areas of pricing and may also be given the opportunity to rotate into other business areas such as reserving in the medium term. This will enable you to become a strong ‘all-rounder’ and add real value to your skill set and CV marketability. International Prospects – my client offers international secondments to other parts of their Group who require assistance in specialist areas. Recent secondments have included Canada, India & Denmark. Centre of Excellence - the UK is the company’s ‘Centre of Excellence’ they regularly host training and development courses for their Group colleagues and welcome a range of highly academic and experienced professionals from around the world. This will give you the chance to work with and learn from a diverse range of people.

Flexible Working – in addition they offer their staff flexible working and a formal flexi-time scheme which enables you to control your working hours. So long as you are in work
during ‘core hours’ (10.00am to 3.00pm) you can pretty much start and finish when you please. Ensuring that you fit work around your life and not the other way around!

The Role
You will be applying statistical principles to general insurance you’ll ideally have knowledge of Actuarial / Statistical techniques, as well as proven data handling skills and a strong mathematical ability. It’s a rewarding role where you’ll make a big impact on their pricing strategy. Additionally, you’ll be able to look forward to significant career opportunities in other UK pricing areas, as well as internationally in the future.

Specific Responsibilities:
Gathering required data (qualitative and quantitative) for analytical and/or modelling purposes, from agreed internal and/or external sources, in line with all mandatory data management, reporting and legal/regulatory requirements.
Cleaning and validating information collected to ensure accuracy and reliability, as far as possible.
Running required calculations, models and analyses using relevant data systems and/or spreadsheets and following agreed processes, policies and procedures.
Analysing information to identify variances, trends, anomalies, etc and explain, highlight and/or refer relevant issues to others as appropriate.
Preparing and presenting required reports, forecasts and other outputs to agreed standards in terms of timeliness, content and presentation on a regular and ad hoc basis as
required.
Taking all reasonable steps to ensure the accuracy and reliability of output, providing appropriate guidance to others on implications and interpretation.

What Do I Need?
Degree in Mathematics, Economics, Statistics or similar (or have relevant work experience) Good commercial awareness and general understanding of the wider financial services market Can combine technical expertise with business knowledge to extrapolate insights from complex or limited data Strong data analysis, communication and relationship management skills.

If you are interested in applying for this role please send your CV in the strictest of confidence. If you would also like to discuss this role in more detail please contact Austin Brislen at Sagar Wright on 0113 234 0500.

Thursday, July 5, 2012

Sample Questions for Linear Models

1. State and prove Gauss-Markov theorem.

2. In Gauss-Markov setup of linear model, $\underline{Y}=X\underline{\beta}+\epsilon$, obtain

a. Any two functions belonging to error space.

b. An expression for the regression sum of squares, SSR and error sum of squares, SSE.

c. The expression for the SSE subject to m-linearity independent conditions $\underline{\lambda}^{'}_{(i)}\underline{\beta}=d_i, i=1,2,…,m$ where $\underline{\lambda}^{'}_{(i)}\underline{\beta} $ is an estimable parametric function.

The expression for the conditional error sum of squares, Explain how it can be used in testing the hypothesis related to it?

d. Various sum of squares as a quadratic form.

3. In Gauss-Markov setup of linear model, $\underline{Y}=X\underline{\beta}+\epsilon$, Show that

a. The solution to the normal equations actually minimizes the residual sum of squares.

b. The system of normal equations is always consistent.

c. The necessary and sufficient condition for estimability of parametric function $\underline{\lambda}^{'}_{(i)}\underline{\beta}$ is $\underline{\lambda}^{'}=\underline{\lambda}^{'}H, \underline{\lambda}^{'}=\underline{\lambda}^{'}X$

d. Every linear parametric function $\underline{\lambda}^{'}_{(i)}\underline{\beta}$ is estimable if and only if rank of $X$ is equal to the number of parameters.

e. Any linear function $\underline{a}^{'}\underline{y}$ of observations such that $\underline{a}$ is linear combination of columns of $X$-matrix is BLUE of its expected value.

f. The covariance between any function belonging to error space and any BLUE is zero.

g. The distribution of SSR is non-central Chi-square. State clearly assumptions made.

h. The distribution of SSE is Chi-square. Further show that it is independently distributed with the distribution of SSR.

i. Expected value of residual sum of squares is $(n-r){\sigma}^2$, where $r$ is rank of the $X$ and $n$ denotes the total number of observations.

j. The BLUE of an estimable parametric function is unique almost surely.

k. The BLUE of an estimable parametric function is independent of residuals.

l. The residuals are not independently distributed

m. Variance-covariance matrix of m-linearly independent estimable parametric functions $\Lambda\hat{\underline\beta} \text{is} \Lambda S^{-}\Lambda^{'}\sigma^2$. Further show that it is non singular, make proper assumptions for developing the results.

n. The difference between the conditional and unconditional error sum of squares is quadratic form in $\underline{y}$ with an idempotent matrix of the form.

4. Discuss the role of functions belonging to error space in getting unbiased estimator of the variance of errors in Gauss Markov model.

5. Define the following

a. Estimation space and estimability of parametric function

b. BLUE

c. Sum of squares due to the hypothesis $H_0$

d. Conditional error sum of squares

e. Full rank model

6. For the linear model $\underline{Y}=X\underline{\beta}+\epsilon$ with $E(\underline{\epsilon})$ and $Cov(\underline{\epsilon})=\sigma^2 D$ where D is symmetric positive definite matrix. Derive BLUE of a linear parametric function, its variance and an expression for SSEReduce this to the standard form and hence obtain $Cov(\hat{\underline\beta})$ where $D=(w_1,w_2,\cdots,w_n)$ is nonsingular matrix. Derive BLUE of a linear parametric function, its variance and an expression for SSE.

7. Obtain condition of estimability of linear parametric function and hence give one set of linearly independent estimable parametric functions with their variances and covariances for

a. One way classification model

b. Two way classification model

8. For completely randomized design using normal equations, obtain the best linear unbiased estimator of an elementary treatment contrast and its variance.

9. Derive F test for testing equality of effects of

a. all treatments in one-way classification model

b. all treatments/blocks in two-way classification model

Express the various sum of squares used in above test in ANOVA table

10. Consider the model $y_i=\mu_i+\varepsilon_i, i=1,2,\cdots,n$ where the parameters $\mu_i$ subject to condition $\sum_{i=1}^n{\mu_i} =0$. Obtain normal equations and their solutions. Is $\mu_i$ is estimable?

11. Given that $y_i$, $i=1,2,\cdots,n$ are independent normal variates with common variance $\sigma^2$ and mean $\mu$. Write the model in Gauss-Markov setup and obtain the test for $H_0:\mu=0.$

12. For one way model with 4 treatment $T_1,T_2,T_3,T_4$ each with observations $n_1,n_2,n_3,n_4$ obtain the following

a. Normal equation and their solution

b. Variance of BLUE of contrast in treatment effects.

13. Consider $E(Y_1 )=\beta_1+\beta_2-\beta_3, E(Y_2)=E(Y_4)=\beta_2-\beta_4, E(Y_3 )=\beta_1+\beta_2$ and $Cov(\underline{Y})=\sigma^2 I_n$

a. Check whether above model is full rank model or non full rank model.

b. Obtain rank of estimation space and rank of error space.

c. Obtain one solution of normal equations and hence obtain BLUE of $\beta_1+\beta_2$ if it is estimable parametric function.

14. Let $y_{ij}=\mu_i+\varepsilon_{ij}, i=1,2,3, j=1,2$ where $\varepsilon_{ij}\sim N(0,\sigma^2)$ Obtain $SSH_0$ for testing the hypothesis $H_0:\mu_1=2\mu_2=3\mu_3$

15. Let $E(Y_1)=2\mu, E(Y_2)=\mu$ and

$Cov(\underline{Y})=$ |2 1|$\sigma^{2}$.

|1 2|

Obtain an unbiased estimator of $\sigma^{2}$.