Ejemplo n.º 1
0
    expected_var_t = t
    expected_cov_st = min(s, t)

elif which_process == 'bbbm' or which_process == 'bbsde':
    expected_mean_s = X0 + s / T * (XT - X0)
    expected_mean_t = X0 + t / T * (XT - X0)
    expected_var_s = s * (T - s) / T
    expected_var_t = t * (T - t) / T
    if s < t:
        expected_cov_st = s * (T - t) / T
    else:
        expected_cov_st = t * (T - s) / T

mean_s = stats_m.find_mean(slice_s)
mean_t = stats_m.find_mean(slice_t)
var_s = stats_m.find_sampvaraux(slice_s, mean_s)
var_t = stats_m.find_sampvaraux(slice_t, mean_t)
cov_st = stats_m.find_sample_covariance(slice_s, slice_t)

diff_mean_s = mean_s - expected_mean_s
diff_mean_t = mean_t - expected_mean_t
diff_var_s = var_s - expected_var_s
diff_var_t = var_t - expected_var_t
diff_cov_st = cov_st - expected_cov_st

if which_process == 'bm':
    print 'T  = %11.7f' % T
elif which_process == 'bbbm' or which_process == 'bbsde':
    print 'X0 = %11.7f' % X0
    print 'XT = %11.7f' % XT
    print 'T  = %11.7f' % T
Ejemplo n.º 2
0
	expected_var_t  = t
	expected_cov_st = min(s, t)

elif which_process == 'bbbm' or which_process == 'bbsde':
	expected_mean_s = X0 + s/T*(XT-X0)
	expected_mean_t = X0 + t/T*(XT-X0)
	expected_var_s  = s*(T-s)/T
	expected_var_t  = t*(T-t)/T
	if s < t:
		expected_cov_st = s*(T-t)/T
	else:
		expected_cov_st = t*(T-s)/T

mean_s = stats_m.find_mean(slice_s)
mean_t = stats_m.find_mean(slice_t)
var_s  = stats_m.find_sampvaraux(slice_s, mean_s)
var_t  = stats_m.find_sampvaraux(slice_t, mean_t)
cov_st = stats_m.find_sample_covariance(slice_s, slice_t)

diff_mean_s = mean_s - expected_mean_s
diff_mean_t = mean_t - expected_mean_t
diff_var_s  = var_s  - expected_var_s
diff_var_t  = var_t  - expected_var_t
diff_cov_st = cov_st - expected_cov_st

if which_process == 'bm':
	print 'T  = %11.7f' % T
elif which_process == 'bbbm' or which_process == 'bbsde':
	print 'X0 = %11.7f' % X0
	print 'XT = %11.7f' % XT
	print 'T  = %11.7f' % T
Ejemplo n.º 3
0
Ybars = []
for rep in range(0, num_sample_reps):
	X = random_sample_with_replacement(population, n)
	Xbar = stats_m.find_mean(X)
	Xbars.append(Xbar)

	Ybar = 0.0
	for i in range(0, k):
		stratum = strata[i]
		ni = nis[i]
		wi = wis[i]

		Xi = random_sample_with_replacement(stratum, ni)
		Xibar = stats_m.find_mean(Xi)
		Ybar += wi * Xibar
	Ybars.append(Ybar)

EXbar = stats_m.find_mean(Xbars)
EYbar = stats_m.find_mean(Ybars)
VXbar = stats_m.find_sampvaraux(Xbars, true_mu)
VYbar = stats_m.find_sampvaraux(Ybars, true_mu)

print "n   = %7d" % (n)
for i in range(0, k):
	print "n_%d = %7d" % (i, nis[i])
print
print "E[Xbar] = %7.4f" % (EXbar)
print "E[Ybar] = %7.4f" % (EYbar)
print "E[(Xbar-m)^2] = %7.4f" % (VXbar)
print "E[(Ybar-m)^2] = %7.4f" % (VYbar)